data mining topics for thesis

82 Data Mining Essay Topic Ideas & Examples

🏆 best data mining topic ideas & essay examples, 💡 good essay topics on data mining, ✅ most interesting data mining topics to write about.

Disadvantages of Using Web 2.0 for Data Mining Applications This data can be confusing to the readers and may not be reliable. Lastly, with the use of Web 2.
Data Mining and Its Major Advantages Thus, it is possible to conclude that data mining is a convenient and effective way of processing information, which has many advantages.
The Data Mining Method in Healthcare and Education Thus, I would use data mining in both cases; however, before that, I would discover a way to improve the algorithms used for it.
Data Mining Tools and Data Mining Myths The first problem is correlated with keeping the identity of the person evolved in data mining secret. One of the major myths regarding data mining is that it can replace domain knowledge.
Hybrid Data Mining Approach in Healthcare One of the healthcare projects that will call for the use of data mining is treatment evaluation. In this case, it is essential to realize that the main aim of health data mining is to […]
Terrorism and Data Mining Algorithms However, this is a necessary evil as the nation’s security has to be prioritized since these attacks lead to harm to a larger population compared to the infringements.
Transforming Coded and Text Data Before Data Mining However, to complete data mining, it is necessary to transform the data according to the techniques that are to be used in the process.
Data Mining and Machine Learning Algorithms The shortest distance of string between two instances defines the distance of measure. However, this is also not very clear as to which transformations are summed, and thus it aims to a probability with the […]
Summary of C4.5 Algorithm: Data Mining 5 algorism: Each record from set of data should be associated with one of the offered classes, it means that one of the attributes of the class should be considered as a class mark.
Data Mining in Social Networks: Linkedin.com One of the ways to achieve the aim is to understand how users view data mining of their data on LinkedIn.
Ethnography and Data Mining in Anthropology The study of cultures is of great importance under normal circumstances to enhance the understanding of the same. Data mining is the success secret of ethnography.
Issues With Data Mining It is necessary to note that the usage of data mining helps FBI to have access to the necessary information for terrorism and crime tracking.
Large Volume Data Handling: An Efficient Data Mining Solution Data mining is the process of sorting huge amount of data and finding out the relevant data. Data mining is widely used for the maintenance of data which helps a lot to an organization in […]
Data Mining and Analytical Developments In this era where there is a lot of information to be handled at ago and actually with little available time, it is necessarily useful and wise to analyze data from different viewpoints and summarize […]
Levi’s Company’s Data Mining & Customer Analytics Levi, the renowned name in jeans is feeling the heat of competition from a number of other brands, which have come upon the scene well after Levi’s but today appear to be approaching Levi’s market […]
Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence This paper aims to review the application of A.I.in the context of blockchain finance by examining scholarly articles to determine whether the A.I.algorithm can be used to analyze this financial market.
“Data Mining and Customer Relationship Marketing in the Banking Industry“ by Chye & Gerry First of all, the article generally elaborates on the notion of customer relationship management, which is defined as “the process of predicting customer behavior and selecting actions to influence that behavior to benefit the company”.
Data Mining Techniques and Applications The use of data mining to detect disturbances in the ecosystem can help to avert problems that are destructive to the environment and to society.
Ethical Data Mining in the UAE Traffic Department The research question identified in the assignment two is considered to be the following, namely whether the implementation of the business intelligence into the working process will beneficially influence the work of the Traffic Department […]
Canadian University Dubai and Data Mining The aim of mining data in the education environment is to enhance the quality of education for the mass through proactive and knowledge-based decision-making approaches.
Data Mining and Customer Relationship Management As such, CRM not only entails the integration of marketing, sales, customer service, and supply chain capabilities of the firm to attain elevated efficiencies and effectiveness in conveying customer value, but it obliges the organization […]
E-Commerce: Mining Data for Better Business Intelligence The method allowed the use of Intel and an example to build the study and the literature on data mining for business intelligence to analyze the findings.
Ethical Implications of Data Mining by Government Institutions Critics of personal data mining insist that it infringes on the rights of an individual and result to the loss of sensitive information.
Data Mining Role in Companies The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations.
Data Warehouse and Data Mining in Business The circumstances leading to the establishment and development of the concept of data warehousing was attributed to the fact that failure to have a data warehouse led to the need of putting in place large […]
Data Mining: Concepts and Methods Speed of data mining process is important as it has a role to play in the relevance of the data mined. The accuracy of data is also another factor that can be used to measure […]
Data Mining Technologies According to Han & Kamber, data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data that in most circumstances is stored in repositories, business databases […]
Data Mining: A Critical Discussion In recent times, the relatively new discipline of data mining has been a subject of widely published debate in mainstream forums and academic discourses, not only due to the fact that it forms a critical […]
Commercial Uses of Data Mining Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations.
A Discussion on the Acceptability of Data Mining Today, more than ever before, individuals, organizations and governments have access to seemingly endless amounts of data that has been stored electronically on the World Wide Web and the Internet, and thus it makes much […]
Applying Data Mining Technology for Insurance Rate Making: Automobile Insurance Example
Applebee’s, Travelocity and Others: Data Mining for Business Decisions
Applying Data Mining Procedures to a Customer Relationship
Business Intelligence as Competitive Tool of Data Mining
Overview of Accounting Information System Data Mining
Applying Data Mining Technique to Disassembly Sequence Planning
Approach for Image Data Mining Cultural Studies
Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues
Database Data Mining: The Silent Invasion of Privacy
Data Management: Data Warehousing and Data Mining
Constructive Data Mining: Modeling Consumers’ Expenditure in Venezuela
Data Mining and Its Impact on Healthcare
Innovations and Perspectives in Data Mining and Knowledge Discovery
Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Linking Data Mining and Anomaly Detection Techniques
Data Mining and Pattern Recognition Models for Identifying Inherited Diseases
Credit Card Fraud Detection Through Data Mining
Data Mining Approach for Direct Marketing of Banking Products
Constructive Data Mining: Modeling Argentine Broad Money Demand
Data Mining-Based Dispatching System for Solving the Pickup and Delivery Problem
Commercially Available Data Mining Tools Used in the Economic Environment
Data Mining Climate Variability as an Indicator of U.S. Natural Gas
Analysis of Data Mining in the Pharmaceutical Industry
Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks
Credit Evaluation Model for Banks Using Data Mining
Data Mining for Business Intelligence: Multiple Linear Regression
Cluster Analysis for Diabetic Retinopathy Prediction Using Data Mining Techniques
Data Mining for Fraud Detection Using Invoicing Data
Jaeger Uses Data Mining to Reduce Losses From Crime and Waste
Data Mining for Industrial Engineering and Management
Business Intelligence and Data Mining – Decision Trees
Data Mining for Traffic Prediction and Intelligent Traffic Management System
Building Data Mining Applications for CRM
Data Mining Optimization Algorithms Based on the Swarm Intelligence
Big Data Mining: Challenges, Technologies, Tools, and Applications
Data Mining Solutions for the Business Environment
Overview of Big Data Mining and Business Intelligence Trends
Data Mining Techniques for Customer Relationship Management
Classification-Based Data Mining Approach for Quality Control in Wine Production
Data Mining With Local Model Specification Uncertainty
Employing Data Mining Techniques in Testing the Effectiveness of Modernization Theory
Enhancing Information Management Through Data Mining Analytics
Evaluating Feature Selection Methods for Learning in Data Mining Applications
Extracting Formations From Long Financial Time Series Using Data Mining
Financial and Banking Markets and Data Mining Techniques
Fraudulent Financial Statements and Detection Through Techniques of Data Mining
Harmful Impact Internet and Data Mining Have on Society
Informatics, Data Mining, Econometrics, and Financial Economics: A Connection
Integrating Data Mining Techniques Into Telemedicine Systems
Investigating Tobacco Usage Habits Using Data Mining Approach
Electronics Engineering Paper Topics
Cyber Security Topics
Google Paper Topics
Hacking Essay Topics
Identity Theft Essay Ideas
Internet Research Ideas
Microsoft Topics
Chicago (A-D)
Chicago (N-B)

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Data Mining Dissertation Topics

The term “data mining” refers to an intelligent data lookup capacity that uses statistics-based algorithms and methodologies to find trends, patterns, links, and correlations within the collected data and records. Audio, Pictorial, Video, textual, online, and social media-based mining are only a few examples of data mining. This article will provide you with a complete overview of various recent data mining dissertation topics . Let us first start with the definition of data mining processes.

What is the data mining process?

The practice of evaluating a huge batch containing data to find different patterns is known as data mining.
Companies can utilize data mining for a variety of purposes, including knowing as to what consumers are engaged in or would like to buy, as well as detection of fraudulent activities and malware scanning.

Hence data mining plays a very significant role in both commercial and personal life aspects of the modern world. We have been working on data mining dissertation topics and project ideas for more than 15 years as a result of which we have gained huge expertise and have acquired vast knowledge, skills, and experience in the field. So we can guide you in all the existing and normal data mining methods and techniques. Let us now talk about the data mining techniques below

Data mining techniques

Neural networks
Rule induction
Nearest neighbor classification
Decision tree
Descriptive techniques – sequential analysis, association, and clustering

Complete explanation and description on all these techniques and methods are available at our website on data mining dissertation topics . By understanding the importance of data mining, we have successfully worked out several advanced projects and implementations in real-time . Check out our website for all details about our successful projects in data mining. Let us now see about the data mining approaches below

Approaches in data mining

Belief nets
Neural nets (Kohonen and backpropagation)
Decision trees (CHAID, CAITT, and C 4.5)
Rules (genetic algorithms and induction)
Case-based reasoning
Nearest neighbor

This is the basic classification of the various data mining approaches that are in use today. With the support of the best engineers and world-class certified experts in data mining , we are here to provide you with a massive amount of reliable and authentic research data along with complete support in interpretation, analysis, and understanding them . Get in touch with us at any time for complete support for your data mining dissertation . We assure to give you full support and ultimate guidance on any data mining dissertation topics. We will now talk about the major issues in data mining

Major issues in data mining

Parallel, distributed, and incremental mining algorithms
Data mining algorithm efficiency and scalability
Incorporation of background data
Interactive meaning
Data mining result presentation and visualization
Pattern evaluation meaning
pattern and Constraint guided mining
Power boosting in networking environment
Data mining interdisciplinary approach
Data insufficiency and uncertainty
Handling the issues of noise
Multidimensional data mining space
Novel approaches and incorporating multiple aspects of data mining

We have handled all these issues efficiently and have devised successful methods to overcome them. Get in touch with us to know more about the potential data mining solutions and advanced techniques used in overcoming the issues of data mining . What are the top data mining topics?

Data Mining Tools

WEKA, Orange, Tanagra and NLTK
Angoss, Oracle, and STATISTICA (or StatSoft)
Pentaho, Rattle, and Apache Mahout
RapidMiner, R – programming, and KNIME
JHepWork, IBM SPSS, and SAS Enterprise Miner

The tips and advice in using these tools of data mining are explained in detail on our website. Also, we are here to help you in handling these data mining tools efficiently with proper demonstrations and explanations. Our engineers have great skills in working with these data mining tools. So reach out to us for any support related to data mining. What are the recent trends in data mining?

Latest trends in data mining

Spatial data mining and semantic web mining
Personalized systems for recommendations and low-quality source data mining
Data retrieval based on content and multimedia retrieval
Graph theory data retrieval and data mining quantum computing
Integration of data warehousing and DNA
Retrieval based on content and audio mining at low quality
Itemset mining for optimization of MapReduce
Analyzing sentiments on social media and P2P
Assessing the quality of multimedia and Internet of Things applications using data mining
Management based on grid databases and Context-aware computing

At present we are offering complete project support and dissertation writing guidance along with assignments, paper publication, proposal, thesis, and many more with proper grammatical checks, full review, and approval. Therefore we are here to help you in all aspects of your data mining research . What are the Datasets available for data mining?

Datasets for Data Mining Projects

It is a data marketplace and open catalog
With infochimps, you shall perform sharing, selling, curative, and data downloading
It has blogs of about forty-four million
It ranges from August to October of 2008
Artificial intelligence-based photos and data collection
Useful for academic and research purposes
Collection of geospatial and geographic data
Artificial intelligence and machine learning-based updated data collection
Data is collected from around ten thousand Europe based companies
It is a repository of molecular abundance and gene expression
It supports MIAME compliances
Retrieving, querying, and browsing data is made possible with this gene expression resource
Collection of stocks and futures-based financial data
Google-based text collection from various books

Apart from these relevant datasets, there are also many other datasets including CIDDS, DAPARA, CICIDS2017, ADFA – IDS, TUIDS, ISCXIDS2012, AWID, and NSL – KDD . Complete information on all these datasets and tips for handling them efficiently will be shared with you as you avail of our services on data mining dissertation topics . Feel free to interact with our experts regarding any doubts in your data mining research. We ensure to solve all your doubts instantly.

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Data Analytics and Machine Learning Group
TUM School of Computation, Information and Technology
Technical University of Munich

Open Topics

We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. A non-exhaustive list of open topics is listed below.

If you are interested in a thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email and we will arrange a meeting to talk about the potential topics.

Graph Neural Networks for Spatial Transcriptomics

Type: Master's Thesis

Prerequisites:

Strong machine learning knowledge
Proficiency with Python and deep learning frameworks (PyTorch, TensorFlow, JAX)
Knowledge of graph neural networks (e.g., GCN, MPNN)
Optional: Knowledge of bioinformatics and genomics

Description:

Spatial transcriptomics is a cutting-edge field at the intersection of genomics and spatial analysis, aiming to understand gene expression patterns within the context of tissue architecture. Our project focuses on leveraging graph neural networks (GNNs) to unlock the full potential of spatial transcriptomic data. Unlike traditional methods, GNNs can effectively capture the intricate spatial relationships between cells, enabling more accurate modeling and interpretation of gene expression dynamics across tissues. We seek motivated students to explore novel GNN architectures tailored for spatial transcriptomics, with a particular emphasis on addressing challenges such as spatial heterogeneity, cell-cell interactions, and spatially varying gene expression patterns.

Contact : Filippo Guerranti , Alessandro Palma

References:

Cell clustering for spatial transcriptomics data with graph neural network
Unsupervised spatially embedded deep representation of spatial transcriptomics
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
DeepST: identifying spatial domains in spatial transcriptomics by deep learning
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder

GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data

Generative Models for Drug Discovery

Type: Mater Thesis / Guided Research

Proficiency with Python and deep learning frameworks (PyTorch or TensorFlow)
Knowledge of graph neural networks (e.g. GCN, MPNN)
No formal education in chemistry, physics or biology needed!

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain which has experienced great attention through the success of generative models. These models promise a more efficient exploration of the vast chemical space and generation of novel compounds with specific properties by leveraging their learned representations, potentially leading to the discovery of molecules with unique properties that would otherwise go undiscovered. Our topics lie at the intersection of generative models like diffusion/flow matching models and graph representation learning, e.g., graph neural networks. The focus of our projects can be model development with an emphasis on downstream tasks ( e.g., diffusion guidance at inference time ) and a better understanding of the limitations of existing models.

Contact : Johanna Sommer , Leon Hetzel

Equivariant Diffusion for Molecule Generation in 3D

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation

Structure-based Drug Design with Equivariant Diffusion Models

Efficient Machine Learning: Pruning, Quantization, Distillation, and More

Type: Master's Thesis / Guided Research / Hiwi

Strong knowledge in machine learning
Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch)

The efficiency of machine learning algorithms is commonly evaluated by looking at target performance, speed and memory footprint metrics. Reduce the costs associated to these metrics is of primary importance for real-world applications with limited ressources (e.g. embedded systems, real-time predictions). In this project, you will investigate solutions to improve the efficiency of machine leanring models by looking at multiple techniques like pruning, quantization, distillation, and more.

Contact: Bertrand Charpentier

The Efficiency Misnomer
A Gradient Flow Framework for Analyzing Network Pruning
Distilling the Knowledge in a Neural Network
A Survey of Quantization Methods for Efficient Neural Network Inference

Deep Generative Models

Type: Master Thesis / Guided Research

Strong machine learning and probability theory knowledge
Knowledge of generative models and their basics (e.g., Normalizing Flows, Diffusion Models, VAE)
Optional: Neural ODEs/SDEs, Optimal Transport, Measure Theory

With recent advances, such as Diffusion Models, Transformers, Normalizing Flows, Flow Matching, etc., the field of generative models has gained significant attention in the machine learning and artificial intelligence research community. However, many problems and questions remain open, and the application to complex data domains such as graphs, time series, point processes, and sets is often non-trivial. We are interested in supervising motivated students to explore and extend the capabilities of state-of-the-art generative models for various data domains.

Contact : Marcel Kollovieh , David Lüdke

Flow Matching for Generative Modeling
Auto-Encoding Variational Bayes
Denoising Diffusion Probabilistic Models
Structured Denoising Diffusion Models in Discrete State-Spaces

Active Learning for Multi Agent 3D Object Detection

Type: Master's Thesis Industrial partner: BMW

Prerequisites:

Strong knowledge in machine learning
Knowledge in Object Detection
Excellent programming skills
Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch)

Description:

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example 3D object detection. To provide promising results, these networks often require a lot of complex annotation data for training. These annotations are often costly and redundant. Active learning is used to select the most informative samples for annotation and cover a dataset with as less annotated data as possible.

The objective is to explore active learning approaches for 3D object detection using combined uncertainty and diversity based methods.

Contact: Sebastian Schmidt

References:

Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
Efficient Uncertainty Estimation for Semantic Segmentation in Videos
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Towards Open World Active Learning for 3D Object Detection

Graph Neural Networks

Type: Master's thesis / Bachelor's thesis / guided research

Knowledge of graph/network theory

Graph neural networks (GNNs) have recently achieved great successes in a wide variety of applications, such as chemistry, reinforcement learning, knowledge graphs, traffic networks, or computer vision. These models leverage graph data by updating node representations based on messages passed between nodes connected by edges, or by transforming node representation using spectral graph properties. These approaches are very effective, but many theoretical aspects of these models remain unclear and there are many possible extensions to improve GNNs and go beyond the nodes' direct neighbors and simple message aggregation.

Contact: Simon Geisler

Semi-supervised classification with graph convolutional networks
Relational inductive biases, deep learning, and graph networks
Diffusion Improves Graph Learning
Weisfeiler and leman go neural: Higher-order graph neural networks
Reliable Graph Neural Networks via Robust Aggregation

Physics-aware Graph Neural Networks

Type: Master's thesis / guided research

Proficiency with Python and deep learning frameworks (JAX or PyTorch)
Knowledge of graph neural networks (e.g. GCN, MPNN, SchNet)
Optional: Knowledge of machine learning on molecules and quantum chemistry

Deep learning models, especially graph neural networks (GNNs), have recently achieved great successes in predicting quantum mechanical properties of molecules. There is a vast amount of applications for these models, such as finding the best method of chemical synthesis or selecting candidates for drugs, construction materials, batteries, or solar cells. However, GNNs have only been proposed in recent years and there remain many open questions about how to best represent and leverage quantum mechanical properties and methods.

Contact: Nicholas Gao

Directional Message Passing for Molecular Graphs
Neural message passing for quantum chemistry
Learning to Simulate Complex Physics with Graph Network
Ab initio solution of the many-electron Schrödinger equation with deep neural networks
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Robustness Verification for Deep Classifiers

Type: Master's thesis / Guided research

Strong machine learning knowledge (at least equivalent to IN2064 plus an advanced course on deep learning)
Strong background in mathematical optimization (preferably combined with Machine Learning setting)
Proficiency with python and deep learning frameworks (Pytorch or Tensorflow)
(Preferred) Knowledge of training techniques to obtain classifiers that are robust against small perturbations in data

Description : Recent work shows that deep classifiers suffer under presence of adversarial examples: misclassified points that are very close to the training samples or even visually indistinguishable from them. This undesired behaviour constraints possibilities of deployment in safety critical scenarios for promising classification methods based on neural nets. Therefore, new training methods should be proposed that promote (or preferably ensure) robust behaviour of the classifier around training samples.

Contact: Aleksei Kuvshinov

References (Background):

Intriguing properties of neural networks
Explaining and harnessing adversarial examples
SoK: Certified Robustness for Deep Neural Networks
Certified Adversarial Robustness via Randomized Smoothing
Formal guarantees on the robustness of a classifier against adversarial manipulation
Towards deep learning models resistant to adversarial attacks
Provable defenses against adversarial examples via the convex outer adversarial polytope
Certified defenses against adversarial examples
Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks

Uncertainty Estimation in Deep Learning

Type: Master's Thesis / Guided Research

Strong knowledge in probability theory

Safe prediction is a key feature in many intelligent systems. Classically, Machine Learning models compute output predictions regardless of the underlying uncertainty of the encountered situations. In contrast, aleatoric and epistemic uncertainty bring knowledge about undecidable and uncommon situations. The uncertainty view can be a substantial help to detect and explain unsafe predictions, and therefore make ML systems more robust. The goal of this project is to improve the uncertainty estimation in ML models in various types of task.

Contact: Tom Wollschläger , Dominik Fuchsgruber , Bertrand Charpentier

Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Predictive Uncertainty Estimation via Prior Networks
Posterior Network: Uncertainty Estimation without OOD samples via Density-based Pseudo-Counts
Evidential Deep Learning to Quantify Classification Uncertainty
Weight Uncertainty in Neural Networks

Hierarchies in Deep Learning

Type: Master's Thesis / Guided Research

Multi-scale structures are ubiquitous in real life datasets. As an example, phylogenetic nomenclature naturally reveals a hierarchical classification of species based on their historical evolutions. Learning multi-scale structures can help to exhibit natural and meaningful organizations in the data and also to obtain compact data representation. The goal of this project is to leverage multi-scale structures to improve speed, performances and understanding of Deep Learning models.

Contact: Marcel Kollovieh , Bertrand Charpentier

Tree Sampling Divergence: An Information-Theoretic Metricfor Hierarchical Graph Clustering
Hierarchical Graph Representation Learning with Differentiable Pooling
Gradient-based Hierarchical Clustering
Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

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Student theses

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3d face reconstruction using deep learning.

Supervisor: Medeiros de Carvalho, R. (Supervisor 1), Gallucci, A. (Supervisor 2) & Vanschoren, J. (Supervisor 2)

Student thesis : Master

Achieving Long Term Fairness through Curiosity Driven Reinforcement Learning: How intrinsic motivation influences fairness in algorithmic decision making

Supervisor: Pechenizkiy, M. (Supervisor 1), Gajane, P. (Supervisor 2) & Kapodistria, S. (Supervisor 2)

Activity Recognition Using Deep Learning in Videos under Clinical Setting

Supervisor: Duivesteijn, W. (Supervisor 1), Papapetrou, O. (Supervisor 2), Zhang, L. (External coach) & Vasu, J. D. (External coach)

A Data Cleaning Assistant

Supervisor: Vanschoren, J. (Supervisor 1)

Student thesis : Bachelor

A Data Cleaning Assistant for Machine Learning

A deep learning approach for clustering a multi-class dataset.

Supervisor: Pei, Y. (Supervisor 1), Marczak, M. (External coach) & Groen, J. (External coach)

Aerial Imagery Pixel-level Segmentation

A framework for understanding business process remaining time predictions.

Supervisor: Pechenizkiy, M. (Supervisor 1) & Scheepens, R. J. (Supervisor 2)

A Hybrid Model for Pedestrian Motion Prediction

Supervisor: Pechenizkiy, M. (Supervisor 1), Muñoz Sánchez, M. (Supervisor 2), Silvas, E. (External coach) & Smit, R. M. B. (External coach)

Algorithms for center-based trajectory clustering

Supervisor: Buchin, K. (Supervisor 1) & Driemel, A. (Supervisor 2)

Allocation Decision-Making in Service Supply Chain with Deep Reinforcement Learning

Supervisor: Zhang, Y. (Supervisor 1), van Jaarsveld, W. L. (Supervisor 2), Menkovski, V. (Supervisor 2) & Lamghari-Idrissi, D. (Supervisor 2)

Analyzing Policy Gradient approaches towards Rapid Policy Transfer

An empirical study on dynamic curriculum learning in information retrieval.

Supervisor: Fang, M. (Supervisor 1)

An Explainable Approach to Multi-contextual Fake News Detection

Supervisor: Pechenizkiy, M. (Supervisor 1), Pei, Y. (Supervisor 2) & Das, B. (External coach)

An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks

Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach)

Anomaly detection in image data sets using disentangled representations

Supervisor: Menkovski, V. (Supervisor 1) & Tonnaer, L. M. A. (Supervisor 2)

Anomaly Detection in Polysomnography signals using AI

Supervisor: Pechenizkiy, M. (Supervisor 1), Schwanz Dias, S. (Supervisor 2) & Belur Nagaraj, S. (External coach)

Anomaly detection in text data using deep generative models

Supervisor: Menkovski, V. (Supervisor 1) & van Ipenburg, W. (External coach)

Anomaly Detection on Dynamic Graph

Supervisor: Pei, Y. (Supervisor 1), Fang, M. (Supervisor 2) & Monemizadeh, M. (Supervisor 2)

Anomaly Detection on Finite Multivariate Time Series from Semi-Automated Screwing Applications

Supervisor: Pechenizkiy, M. (Supervisor 1) & Schwanz Dias, S. (Supervisor 2)

Anomaly Detection on Multivariate Time Series Using GANs

Supervisor: Pei, Y. (Supervisor 1) & Kruizinga, P. (External coach)

Anomaly detection on vibration data

Supervisor: Hess, S. (Supervisor 1), Pechenizkiy, M. (Supervisor 2), Yakovets, N. (Supervisor 2) & Uusitalo, J. (External coach)

Application of P&ID symbol detection and classification for generation of material take-off documents (MTOs)

Supervisor: Pechenizkiy, M. (Supervisor 1), Banotra, R. (External coach) & Ya-alimadad, M. (External coach)

Applications of deep generative models to Tokamak Nuclear Fusion

Supervisor: Koelman, J. M. V. A. (Supervisor 1), Menkovski, V. (Supervisor 2), Citrin, J. (Supervisor 2) & van de Plassche, K. L. (External coach)

A Similarity Based Meta-Learning Approach to Building Pipeline Portfolios for Automated Machine Learning

Aspect-based few-shot learning.

Supervisor: Menkovski, V. (Supervisor 1)

Assessing Bias and Fairness in Machine Learning through a Causal Lens

Supervisor: Pechenizkiy, M. (Supervisor 1)

Assessing fairness in anomaly detection: A framework for developing a context-aware fairness tool to assess rule-based models

Supervisor: Pechenizkiy, M. (Supervisor 1), Weerts, H. J. P. (Supervisor 2), van Ipenburg, W. (External coach) & Veldsink, J. W. (External coach)

A Study of an Open-Ended Strategy for Learning Complex Locomotion Skills

A systematic determination of metrics for classification tasks in openml, a universally applicable emm framework.

Supervisor: Duivesteijn, W. (Supervisor 1), van Dongen, B. F. (Supervisor 2) & Yakovets, N. (Supervisor 2)

Automated machine learning with gradient boosting and meta-learning

Automated object recognition of solar panels in aerial photographs: a case study in the liander service area.

Supervisor: Pechenizkiy, M. (Supervisor 1), Medeiros de Carvalho, R. (Supervisor 2) & Weelinck, T. (External coach)

Automatic data cleaning

Automatic scoring of short open-ended questions.

Supervisor: Pechenizkiy, M. (Supervisor 1) & van Gils, S. (External coach)

Automatic Synthesis of Machine Learning Pipelines consisting of Pre-Trained Models for Multimodal Data

Automating string encoding in automl, autoregressive neural networks to model electroencephalograpy signals.

Supervisor: Vanschoren, J. (Supervisor 1), Pfundtner, S. (External coach) & Radha, M. (External coach)

Balancing Efficiency and Fairness on Ride-Hailing Platforms via Reinforcement Learning

Supervisor: Tavakol, M. (Supervisor 1), Pechenizkiy, M. (Supervisor 2) & Boon, M. A. A. (Supervisor 2)

Benchmarking Audio DeepFake Detection

Better clustering evaluation for the openml evaluation engine.

Supervisor: Vanschoren, J. (Supervisor 1), Gijsbers, P. (Supervisor 2) & Singh, P. (Supervisor 2)

Bi-level pipeline optimization for scalable AutoML

Supervisor: Nobile, M. (Supervisor 1), Vanschoren, J. (Supervisor 1), Medeiros de Carvalho, R. (Supervisor 2) & Bliek, L. (Supervisor 2)

Block-sparse evolutionary training using weight momentum evolution: training methods for hardware efficient sparse neural networks

Supervisor: Mocanu, D. (Supervisor 1), Zhang, Y. (Supervisor 2) & Lowet, D. J. C. (External coach)

Boolean Matrix Factorization and Completion

Supervisor: Peharz, R. (Supervisor 1) & Hess, S. (Supervisor 2)

Bootstrap Hypothesis Tests for Evaluating Subgroup Descriptions in Exceptional Model Mining

Supervisor: Duivesteijn, W. (Supervisor 1) & Schouten, R. M. (Supervisor 2)

Bottom-Up Search: A Distance-Based Search Strategy for Supervised Local Pattern Mining on Multi-Dimensional Target Spaces

Supervisor: Duivesteijn, W. (Supervisor 1), Serebrenik, A. (Supervisor 2) & Kromwijk, T. J. (Supervisor 2)

Bridging the Domain-Gap in Computer Vision Tasks

Supervisor: Mocanu, D. C. (Supervisor 1) & Lowet, D. J. C. (External coach)

CCESO: Auditing AI Fairness By Comparing Counterfactual Explanations of Similar Objects

Supervisor: Pechenizkiy, M. (Supervisor 1) & Hoogland, K. (External coach)

Clean-Label Poison Attacks on Machine Learning

Supervisor: Michiels, W. P. A. J. (Supervisor 1), Schalij, F. D. (External coach) & Hess, S. (Supervisor 2)

Data Mining Project Ideas

Data mining is the process of analyzing data in large size which are usually unordered and to find some of the relation between them. In order to learn more about process you can read this research paper completely which is based on Data mining.

Define Data Mining

Data mining involves exploring and analyzing data’s in large volume in order to find the patterns followed, hidden correlation, trends and the understanding about the project. This follows some special statistical and computational techniques for collecting information from such a big dataset and also in prediction, decision making and discovering new knowledge from science, research and business.

What is Data Mining?

This process is very helpful in identifying trends and patterns from a big dataset with help of different algorithms and techniques. It is useful for analyzing the data, to find valuable information and to decode a complex or unstructured source of data.

Where Data Mining is used?

In this section we are going to discuss about the uses of Data Mining process. It is used in many different areas and fields in several applications, from which some of them are listed here: Marketing and Business, Education, Scientific research, Healthcare, Environmental science, E-commerce and finance.

Why Data Mining is proposed? Previous Technology Issues

Moving on to the next section, here we are going to discuss about the reason for the proposal of this technology and the challenges faced by this technology. This was proposed so that the process of analyzing and collecting data from larger dataset becomes easy. This technology helps institutions an business for making decisions based on data, improve efficiency and to gain more knowledge about the data which will lead to better results.

The challenges and issues faced by the earlier technologies of data mining include:

Scalability: Because of issues faced by earlier system in storage capacity and high computational power, processing of complex dataset was most challenging.

Data Quality: Problems related to data quality like missing values, inconsistencies and noise leads to difficulty in data mining.

Complex Algorithms: The algorithms used for data mining in earlier stages were more intensive and complex which makes them difficult to run effectively.

Interpretability: Some of the models produced in data mining like deep learning are hard for interpreting, so it could not be adaptable in all fields.

Primary Concerns: The privacy and security of sensitive data should be concerned which leads to challenges in regulation.

Algorithms / Protocols

After knowing about the technology, uses of it and the issues faced by them in the earlier stage, now we are going to learn about the algorithms used for this technology. The algorithms provided for Data mining to overcome the previous issues faced by it are: “Distributed Adaptive Trust-based authentication”, “Hybrid Gray Level Co-occurrence Matrix Fast Fourier Transform” (HGLCM-FFT), “Particle Swarm Optimized Symmetrical Blowfish” (PSOSB) and “Hierarchical Gradient Boosted Isolation Forest” (HGB-IF).

Comparative study / Analysis

The comparative study is done in order to find the best suitable algorithm for that system to overcome the issues face by them in earlier technologies. The previous method faced trust issues in the cloud data. In the proposed work, for each process separate different algorithms are used to overcome the trust issue. Techniques like Normalization, Feature encoding and Dimensionality reduction are used in processing data. For feature extraction “Hybrid Gray Level Co-occurrence” and “Matrix Fast Fourier Transform” (HGLCM-FFT) are used. Making use of Information gain (IG), Symmetric uncertainty, Chi-squared and Gain ratio can help for feature selection. For increasing trust in cloud data, algorithms like Hierarchical Gradient Boosted Isolation Forest (HGB-IF) and “Distributed Adaptive Trust-based authentication method” are used. For data encryption “Particle Swarm Optimized Symmetrical Blowfish” (PSOSB) algorithm is used.

Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by Data mining in the above section are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Attack Detection Rate, CPU usage, Decryption time, Encryption time, False alarm rate, Network usage, Throughput and True positive rate.

Dataset LINKS / Important URL

Here are some of the links provided for you below to gain more knowledge about Data mining which can be useful for you:

https://www.hindawi.com/journals/wcmc/2022/7272405/
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10162196
https://www.mdpi.com/2504-2289/6/4/101
https://www.mdpi.com/2079-9292/12/11/2427
https://www.mdpi.com/1999-5903/14/12/354
Data Mining Applications

In this next section we are going to discuss about the applications of Data mining. This technology has been employed in many industries, from which some of them are listed here: Astronomy, Customer Relationship Management (CRM), Crime Analysis, Education, Environmental Monitoring, Fraud Detection, Telecommunications and Supply Chain Management.

In this study, topology refers to the organization or the structuring of data. There are many types of topology used in structuring data suitable for each context, from which some of them are listed here: Graph Topology, Geometric Topology, Network Topology, Spatial Topology, Sensor Network Topology, Textual Topology, Temporal Topology and Topological Data Analysis (TDA).

Environment

The process of data mining can be done in several tools and environments like SAS or R, different programming languages like Python, specialized tool for data mining like Rapid Miner, cloud service such as AWS and data platforms such as Spark and Hadoop. This can be functioning in various other areas also such as tools for business intelligence, tools for spatial data and tools for text mining, based on the specific requirements. The environment in which these tools function properly depends on certain factors such as complexity, analysis type and data volume.

Simulation Tools

Here we provide the simulation software for Data mining, which is established with the usage of tool like Python of version 3.11.4, to enhance its performance.

After going through this research based on Data mining, which provide lot of information, you can utilize them to clarify the doubts you have about its technology, applications of this technology, and different topologies of it, algorithms followed by it also about the limitations and how it can be overcome.

Data Mining Project Ideas & Topics

The Significance of using Data Extraction Methods for an Effective Big Data Mining Process
Application of Data Mining Technology in Financial Data Analysis Methods under the Background of Big Data
Big Data Mining Algorithm of Internet of Things Based on Artificial Intelligence Technology
Research on The Transformation and Development of K9 Education and Training Institutions under Xuzhou Double Reduction Policy based on Data Mining Technology
Exploring Research Opportunities to Apply Data Mining Techniques in Software Engineering Lifecycle
Effective Multi-Data-Set Kernel Culture System Development in Data Mining
Research on Medical Big Data Mining and Intelligent Analysis for Smart Healthcare
An Exploration of an Operational Multi-Data-Set Kernel Culture Scheme for Practice in Data Mining
Research on Multi-XCTDs Measurement Information Receiving and Data Mining System
Predictive maintenance project implementation based on data-driven & data mining
A Novel Data Mining Algorithm for Power Marketing Information
Design of Analysis Platform for College Students’ Physical Learning Effect Based on Data Mining Algorithm
Boosted Hybrid Privacy Preserving Data Mining (BHPPDM) Technique to Increase Privacy and Accuracy
Extracting Behavioral Characteristics of College Students Using Data Mining on Big Data
Construction of scientific and technological innovation enterprise management information system under big data mining technology
Design of TCM Research Demand System Based on Data Mining Technology
Analysis of K-means and K-DBSCAN Commonly Used in Data Mining
Data Mining of Prescription Rules for Six Basic Diseases of Mongolian Medicine Based on Decision Tree
Detection of Behavioral Patterns of Viral Hepatitis Using Data Mining
Teaching Resource Sharing System in OBE Mode Based on Data Mining Technology
Machine Learning based Data Mining for Detection of Credit Card Frauds
Digit-DM: A Sustainable Data Mining Modell for Continuous Digitization in Manufacturing
Digitization of Emergency Monitoring Processes and Data Mining
Public Comment Analysis Model of Network Media Based on Big Data Mining and Implementation Plans
The application of data mining techniques for predicting education to new undergraduate students at Chiang Mai Rajabhat University
A Multi-Label Classification Method Based On Textual Data Mining
Implementation of Railway Accident Judgment Criteria Optimization Based on Data Mining and Digital Programming Technology
Waste Miner: An Efficient Waste Collection System for Smart Cities Leveraging IoT and Data Mining Technique
A Review of Time Series Data Mining Methods Based on Cluster Analysis
Application of Data Mining Technology in the Analysis of CET-4 Scores
A Method of Filling Missing Values in Data using Data Mining
Predicting Student’s Academic Performance Using Data Mining Methods: Review Paper
Application of Machine Learning in Data Mining under the Background of Big Data
Hybrid Clustering Techniques for Optimizing Online Datasets Using Data Mining Techniques
Remote monitoring method of deep foundation pit operation equipment based on AIOT technology and data mining
Research and Practice of Enterprise Education Mode in Universities Based on Data Mining
Vehicle Trajectory Data Mining for Artificial Intelligence and Real-Time Traffic Information Extraction
A DAG-NOTEARS-based Data Mining Method for Faulty Samples
Research on Personalized Recommendation Algorithm of Tourism E-commerce Platform Products Based on Data Mining
Review of Data Mining Techniques in Performance Prediction for Medical Schools
English pronunciation quality evaluation system based on data mining algorithm
Detection of Early Fault in Power Electronic Converters through Machine Learning and Data Mining Techniques
Brain-like Intelligent Data Mining Mechanism Based on Convolutional Neural Network
Implementation Data Mining with the Naive Bayes Classifier Algorithm in Determining the Type of Stroke
Improve Data Mining Performance by Noise Redistribution: A Mixed Integer Programming Formulation
Enhancing the detection of fraudulent activities in the distribution of energy through data mining algorithms
An Analysis of Cancer Data Sets Utilizing Data Mining
Optimization techniques for preserving privacy in data mining
Multiple Agents based Disaster Prediction for Public Environments using Data Mining Techniques
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Research Topics in Data Mining

Research Topics in Data Mining provide you innovative and newfangled ideas to explore your knowledge in research. We have a research team that consists of top-level experts and versatile developers to provide precise research guidance for research scholars and students. We have 5000+ happy customers from all over the world, and still, we are providing support for them through our 120+ branches situated in various countries. And also, we attained the world’s topmost position among other renowned institute in research.

We also have a tie-up with the standard universities and colleges to provide the best research guidance for the research scholars and students. And also, We have to deal with a complex problem to provide an accurate solution with the help of our experienced professionals in research. We never give up on your research concepts once we committed to you. Our work will prove who we are? What is our standard? How was the experience with us?

Topics in Data Mining

Research Topics in Data Mining offer you nurture platform to shine your research career successfully. Data mining is used to mining meaningful information from large datasets. So we provide support for knowledge mining, i.e., we mine the best and innovative ideas for your project with our top experts’ help. We also provide support for all the students like undergraduate (BE, BTech) postgraduate (ME, MTech, MCA), and research scholars (MPhil, MS, PhD).

We also support research scholars and students in various data mining domains like frequent itemset mining, web mining, opinion mining, etc. In the past 10+ years, we are working in this field, and also, many students are benefited from our smart work. Now let’s have a glance at data mining for your review,

—”Data mining is a technique which is also used to extract meaningful information from a huge database. It is used in many research areas, including mathematics, cybernetics, genetics, and also marketing. The benefit of data mining is to find uncover hidden patterns and also relationships in data that can be used to make predictions that impact businesses.”

Key Features of Data-Mining

Visualization, learning and also statistical techniques support
Focus on large data set and also databases
Automatic discovery of patterns
Python, R, Lisp/Clojure, SQL, also Matlab programming languages used

Graphical user interface support

Rattle gui:.

Free and open source package
R statistical software and also programming support
Used for statistical analysis or model generation
It allows for the dataset to be partitioned [training, validation and also testing]
Machine learning algorithms serves as collection
Open source software also based on java
Primarily designed as a tool for analyzing data [agricultural domains]
Its also graphical user interfaces allow user to easy use.

Oracle Data Miner GUI:

Oracle SQL developer extension
Automation, scheduling and also deployment using SQL and PL/SQL scripts
It enables data analysts to view their data and also accelerate model deployment
Used to built and also evaluate multiple machine learning/data mining models
Open source software (Python based)
Data analysis and also visualization purpose
Support python library also for data manipulation and widget alteration in advanced users
Used to create lining predefined or user-designed widgets

Rapid Miner:

Java based open source platform
Predictive analysis purpose
Used in client/server model with the server offered as either on-premise, or in public or private cloud infrastructures

Database Used

Oracle database 12c:.

It is also purposely designed for the cloud
Composed of oracle database 11g release 2, SQL developer, also Data Miner Repository
Faster and simpler, schema-based consolidation without changes to existing applications

SQL Server:

Provide add-in for:

Microsoft office Visio 2010 (Data mining templates)
Micro-soft office excel (Table analysis tools and also data mining clients)

Apache Mahout:

Data mining library and also scalable learning
Scalable machine learning and also data mining
Managing, writing and also learning large datasets
It provide data summarization, query and also analysis

Most Commonly Used Algorithms and Methods:

Neural Networks
Genetic algorithms
Nearest neighbor method
Semi-supervised learning
Rule reduction
Data visualization
Statistical algorithm
Regression algorithm

-Stepwise regression

-Logistic regression

-Locally estimated scatter plot smoothing

-Ordinary least squares regression

-Linear regression

-Multivariate adaptive regression splines

Supervised learning
Instance based algorithms

-K-Nearest Neighbor

-Locally weighted learning

-Learning vector quantization

-And also in Self-organizing map

RSM and CHAID
EM algorithm

Frameworks and Libraries Used

Apache Mahout
Microsoft OLE DB also for Data mining
Text Analyst COM
PolyAnalyst COM
Data Mining template library
Knowledge STUDIO SDK
NAG Data mining components
Machine learning framework
Wiz [Open java data mining and also knowledge discovery platform]
Java data mining package
Dlib C++ library
Mloss [Machine learning open source software]
YCML [Optimization and also machine learning algorithms]
Very fast machine learning library
Scikit learn
XELOPES [Also For embedded data mining]

Recent Research Applications

Market Basket Analysis
Educational data mining
Manufacturing engineering
Bio informatics
Research analysis [Data pre-processing, data cleaning and also database integration]
Criminal investigation
Customer segmentation applications
Corporate surveillance and also financial banking
Sentiment analysis and also Lie detection
Intrusion detection and also fraud detection
Customer relationship management
Education data mining
Future health care also based on applications

Approaches used:

-Machine learning

-Statistics

-Data visualization

-And also Machine learning

Recent Research Topics

Data mining techniques also for bankruptcy prediction
Data mining and forensic techniques also for an internal intrusion detection and protection system
Mining frequent itemsets on temporal data also using new methodology
Diabetes therapy management by data stream mining also using real time decision rules
Hashing and lexicographie order in hardware also for approximate frequent itemsets mining on data streams
Data mining and web technology also for department automation system
Building cooling load prediction and also for energy efficiency improvement using mining big building operational data
Business intelligence also using an advanced inventory data mining system
Data mining for effect of temperature and also rainfall of paddy yield

The above information will give you an understanding of Data-Mining to get a clear vision of data mining. We also provide additional support for Project development, Thesis writing, Journal paper writing and also Journal publication, etc. If you also have any questions or comments, please get in touch with us. Our tutors are also waiting for communication with you to provide support for your research convenience. Our online service is available 24×7.

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Data Mining Thesis Ideas

Data mining is the technique in which computer-based deep learning and machine learning methodologies are used for automatic analysis and extraction of relevant and useful information from raw data. This article provides a clear picture of data mining thesis ideas wherein complete clarity on data mining thesis can be obtained. Let us first start with an outline on data mining

Outline of data mining

Data mining is amongst the most effective approaches for programmers, academics, and consumers to derive valuable data from enormous collections of data
The process of data discovery includes data cleansing, data aggregation, data gathering, data conversion, data analysis, evaluating patterns, and data display .
It is the process of assessing unseen trends of various data perspectives in order to categorize them into meaningful results
These are then collected and compiled in particular areas like data warehouses, authentic assessments, algorithms for data mining, and other data available in order to reduce costs and increase profits.

As a result, data mining is one of the significant fields of study and areas of research that has the potential to make your career extraordinarily interesting and successful. Since data mining has all the power to analyze the present, past, and future World, it is the key approach taken up by a maximum number of researchers, organizations, and individuals . It is in the field of data mining research that our experts and engineers have been present in for the past two decades. Let us now understand the purpose of data mining

Purpose of data mining

Data mining is an important aspect of the BI solution
Data mining is useful in extracting the most relevant information out of any raw information
It is used in enabling discovery, exploration, and production of data which in turn lead to the discovery of relevant knowledge

Likewise the above and previous discussion, data mining is really useful in solving many real time problems and issues . The customized research support in all data mining thesis ideas provided by us has earned us a huge reputation among the top research scholars of the world. We will now see about the data mining types

Types of data mining

Graph mining is the process of extracting meaningful patterns from graphs.
The resulting data collected could then be utilized to perform cluster as well as classification analysis.
It has a wide range of applications, including biological networks, online data, and cheminformatics, to name a few.
It can be one of the leading research subjects for you.
Web mining is a data mining approach wherein a data analyst searches the internet for data trends.
This is further divided into three categories
Opinion mining often called sentiment mining, is a data mining tool for determining client emotions toward a specific product.
Surveying, open assessments, social networks, healthcare organizations, advertising, and other areas benefit from its usage.
Usage mining – analyzes facts from the user’s computer
Content mining – identifies similarities in data obtained by a web browser
Structural mining – investigates the webpage database schema
The data gathered from the web mining process is then analyzed using methods such as segmentation, classification, and others.

The relevant algorithms and appropriate steps for implementation of all the above data-mining types are readily explained and demonstrated to you once you get in touch with us. Also, more clear explanations about these data mining types are available on our website on data mining thesis ideas . We provide the most confidential research support with in-depth Research and Analysis . What are the research gaps in data mining?

Research Gaps in Data Mining

Interpretation – Model understandability and its insight
Robust nature – missing and noise value handling
Transformation of data – normalization and generalization
Accuracy – prediction and classification accuracy
Scalability – disk-resident database efficiency
Analyzing data relevance – feature selection and irrelevant attribute removal
Speed – time for training, classifying, and predicting the model (construction and usage)
The efficiency of rules – the size of decision tree and classification rule compactness
Data cleaning – data pre-processing, missing value handling, and noise reduction

You can overcome these difficulties with the support of our expert technical team of qualified and experienced engineers and data analysts. We are one of the very beautiful tested and reliable online result guidance providers in data mining. Our customer support facility functions 24/7 with utmost dedication and commitment. So you can get your doubts clarified at all times. Let us now discuss the four techniques in data mining

What are the four data mining techniques?

Predictive data classification
Regression-based on prediction
Description based data clustering
Descriptive association rule discovery

Explanation and description of these techniques are available on our website. With references from benchmark sources and updated information from reputed top journals , we will make your work of data mining thesis and research paper writing easier. Let us now discuss different data mining methods and their general characteristics

The processing time is very fast which is not dependent on the data objects but on the grid size
You can use grid data structure with multi-dimensional resolution
Outlier filtration
Minimal neighborhood points (cluster density)
Low-density regions are used in separating two different clusters
Arbitrary cluster shape finding
Errors in splitting and merging cannot be corrected
Micro clustering and object linkages are incorporated
Multilevel decomposition of hierarchy in clustering
Based on distance
Efficiency in analyzing data sets of size ranging between small and medium
Sporadically shaped mutually exclusive clusters are identified

Novel ideas of data mining research are developing out of these basic data mining approaches. We ensure to provide all sorts of support for all creative and novel data mining thesis ideas . Thorough grammatical checks and multiple remissions are also offered by us. So you can totally depend on us for all your research needs. Let us now discuss data classification

How to classify data in data mining?

Data mining based classification of data consist of two different steps

Future and unknown object classification
Known test sample and result classification are compared
Rate of accuracy (correctly classified model percentage)
Independent test set to avoid overfitting
With acceptable accuracy, the model is used in data classification with unknown labels
Representation of models in the form of decision trees, classification rules, and mathematics-based formulations
Class label attributes based on predefined class (on the basis of assuming samples)
Training set tuple based construction of models

In these ways, data mining-based systems classify the raw data efficiently. You will gain more insights about data mining classification if you look into our successfully delivered projects in the field wherein we have used many different methods to classify and analyze data . Get in touch with us for any kind of assistance regarding the data mining thesis and research. What are the latest trends in data mining?

Latest Trends in Data Mining

Exoplanet discovery and space exploration using image data mining
Autonomous healthcare radiology measures
Recognition of objects and automatic solutions to many questions
Autonomous video games (DQN, Marl, and O)
Reinforcement learning (deep learning-based)
Graphical models (nonparametric and probabilistic)
Tensor based methods
Deep neural nets

Data mining research is developing every day with future scope for future expansion . Therefore as a researcher, it’s important for you to keep yourself highly updated. In this regard, our developers and Research experts will provide you with all information from relevant and updated sources. Also, our formatting and editing team will help you in advance analytics. Let us now talk about the latest data mining thesis topics.

Latest Top 4 Data Mining Thesis Ideas

Discrepancies in the trends seen in the environment are not uncommon, unforeseen, or astonishing.
One of the major components of modern data analysis is detecting, comprehending, and forecasting abnormalities from data.
Active anomaly detection provides for the extraction of crucial data from unstructured data, which may subsequently be used for a number of purposes, such as detecting and repairing flaws in complicated systems, and better understanding the characteristics of natural, sociological, and artificial processes.
People are able to accurately measure, and items change significantly (with notable exceptions).
A heartbeat, that depicts a variation in the cardiac rhythm, is a well-known instance.
A “time series” is a series of similar temporal measures.
Other well-known instances are a politician’s favor growing and declining, or the temperatures rising and falling from over brief (every day), intermediate (every year), and longer-term (every decade), or drifting in climatic conditions
With more inaccuracy, inconsistencies, and false data, there are more issues when it comes to the reliability of data collected
More damages and losses are incurred as these data can mislead your decisions
The real-world data is raw in nature and cannot be expected to be clear and without noise
All the problems associated with such data is yours done improvising the quality, reliability and trustworthiness of the data collected
Advanced Machine Learning techniques containing countless attributes are required to assimilate huge datasets and provide accurate prediction insights (like high-dimensional factorization, intermediary models, and reasoning algorithms) as a result of the advent of Big Data.
As a result, best machine learning and deep learning systems are now being required to learn comprehensive systems with tens of billions of elements.
An ML framework often continues to plan on distributed clusters with tens and thousands of machines to assist the algorithmic requirements of machine learning techniques at these levels
However, trying to implement algorithms and composing software systems for these kinds of distributed clusters requires significant architectural and prototyping exertion.

PhD Research Guidance for Data Mining Thesis

Currently, we are offering Full support for all the above recent data mining thesis ideas . We also support paper writing , assignment help, system development support, research proposal writing, and conference paper writing in all the latest data mining thesis topics and Research ideas. Reach out to us and avail the most sought and trusted online research guidance in data mining. We shall now talk about data mining tools and programming languages

Programming languages and tools for data mining

Java-based machine learning software application
Unstructured information management architecture
Unstructured audio, textual, and video data analysis
IBM is the developer
Lua based open-source deep-learning library
Provides machine learning algorithm support and scientific computing structure
Python-based open-source machine learning library
GNU based data mining, graphical and statistical computing software environment

We are here to help you in developing algorithms and implementing codes in all the above programming languages that are very much needed for all data mining research projects. With more than ten thousand happy customers we are serving the research scholars and students from top universities of the world in their research data mining thesis ideas . So get in touch with us with more confidence and trust.

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Master thesis topics [closed]

I am looking for a thesis to complete my master, I am interested in Predictive Analytics in marketing, HR, management or financial subject, using Data Mining Application.

I have found a very interesting subject: "Predicting customer churn using decision tree" or either "Predicting employee turnover using decision tree", I looked around very hard but unfortunately couldn't find any relevant dataset to download ( Telecommunication Customer churn Dataset ).

I would like to work on a similar subject using "Decision Tree Technique".

Please suggest some topics or project that would make for a good masters thesis subject.

data-mining
predictive-modeling
decision-trees

2 Answers 2

This is the approach I took:

Find journals related to your field of studies
Skim through the proceedings, see if there are titles that catch your interest
Read the papers (carefully or globally) that seemed interesting
Carefully consider the approaches and whatever future suggestions they present in their papers
Think critically: What would you change? What do you want to find out? Don't limit yourself to data but rather orient from the perspective of research. Solutions for data might only become apparent when you know exactly what you want to examine.

I think this has advantages because these papers outline details regarding data as well -- perhaps you can use the same.

Present some papers and your idea to your prospective supervisor and he/she will make some suggestions. Researchers generally have a lot of knowledge about the possibilities and might even be curious about some things themselves.

Good luck! And enjoy.

First, talk to your thesis advisor before committing to a project. They know better than I do.

Secondly, just analyzing a new dataset using standard techniques doesn't make for a good masters thesis. Your project is expected to use some sort of novel approach.

With that said, I'd suggest that you start by reading up on existing decision tree techniques, learning why they work and what their flaws are, and try to find ways to overcome the flaws. Then, once you have your improvement, it should be relatively easy to find a dataset to apply it to.

Not the answer you're looking for? Browse other questions tagged data-mining predictive-modeling bigdata decision-trees research or ask your own question .

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Data mining research topics for ms phd.

I am sharing with you some of the research topics regarding data mining that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

Categorizing the research into 4 categories in this tutorial

Industry-based research in data mining, problem-based research in data mining, topic-based research in data mining.

900+ research ideas in data mining

List of some famous Industries in the world for industry-based research in data mining

Automobile Wholesaling
Pharmaceuticals Wholesaling
Life Insurance & Annuities
Online Computer Software Sales
Supermarkets & Grocery Stores
Electric Power Transmission
IT Consulting
Wholesale Trade Agents and Brokers
Retirement & Pension Plans
Petroleum Refining
New Car Dealers
Drug, Cosmetic & Toiletry Wholesaling
Pharmacy Benefit Management
Property, Casualty and Direct Insurance
Colleges & Universities
Public Schools
Warehouse Clubs & Supercenters
Health & Medical Insurance
Gasoline & Petroleum Wholesaling
Gasoline & Petroleum Bulk Stations
Commercial Banking
Real Estate Loans & Collateralized Debt
E-Commerce & Online Auctions
Electronic Part & Equipment Wholesaling

List of some problems for research in data mining.

Crime Rate Prediction
Fraud Detection
Website Evaluation
Market Analysis
Financial Analysis
Customer trend analysis
Data Warehouse and DBMS
Multidimensional data model
OLAP operations
Example: loan data set
Data cleaning
Data transformation
Data reduction
Discretization and generating concept hierarchies
Installing Weka 3 Data Mining System
Experiments with Weka – filters, discretization
Task relevant data
Background knowledge
Interestingness measures
Representing input data and output knowledge
Visualization techniques
Experiments with Weka – visualization
Attribute generalization
Attribute relevance
Class comparison
Statistical measures
Experiments with Weka – using filters and statistics
Motivation and terminology
Example: mining weather data
Basic idea: item sets
Generating item sets and rules efficiently
Correlation analysis
Experiments with Weka – mining association rules
Basic learning/mining tasks
Inferring rudimentary rules: 1R algorithm
Decision trees
Covering rules
Experiments with Weka – decision trees, rules
The prediction task
Statistical (Bayesian) classification
Bayesian networks
Instance-based methods (nearest neighbor)
Linear models
Experiments with Weka – Prediction
Basic issues in clustering
First conceptual clustering system: Cluster/2
Partitioning methods: k-means, expectation-maximization (EM)
Hierarchical methods: distance-based agglomerative and divisible clustering
Conceptual clustering: Cobweb
Experiments with Weka – k-means, EM, Cobweb
Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
Bayesian approach to classifying text
Web mining: classifying web pages, extracting knowledge from the web
Data Mining software and applications

Research Topics Computer Science

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What is data mining? What is not data mining?
Data Stream Mining - Data Mining
Data Quality in Data Preprocessing for Data Mining
Frequent pattern Mining, Closed frequent itemset, max frequent itemset in data mining
Cloud Computing Research Topics for MS PhD
Semantic Web Research Topics for MS PhD

Latest Thesis Topics in Data Mining

Data mining is an approach for spotting anomalies in huge amounts of data. The legal data contains the specifics of ...

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Data mining is an approach for spotting anomalies in huge amounts of data. The legal data contains the specifics of the crime. Data mining could be used to find patterns and themes in an attempt to forecast what will happen in the future. Machine learning and deep learning techniques and implementations, like web page recommender systems and programmable technology, are built using data mining. Through this article, we have provided an ultimate view on developing any thesis topics in data mining efficiently. We shall first start with an introduction to data mining

INTRODUCTION OF DATA MINING

We require data mining to extract relevant insights from the imbalanced and noisy datasets, which is done in a stage-wise process procedure as follows:

First discard inconsistencies in data
Then uncover patterns related to the analysis work
Then translate data into KDD-friendly formats
Ultimately visualize accumulated data for the user.

In a nutshell, data mining is the process of examining enormous amounts of data autonomously for regularities that go far beyond basic comparison. To separate the data and determine the likelihood of an event, data mining employs simple computational models in the form of algorithms. After all, one must remember that Knowledge Discovery in Data Mining is another name for data mining (KDD).

The following are the major characteristics of data mining

Predictions related to expected results.
Automatic pattern finding
Concentrate on big data sets, databases, and systems.
The generation of actionable and performable insights

Data mining could provide answers to queries that are not easily answered using traditional search and methodologies of reporting. To be more specific, Data Mining allows users to traverse database and data warehouse architectures, data models, and database systems, assess mining trends, and visualize them in various ways. To understand the advantages of data mining you need to have a better idea of the major processes and steps involved in it.

What are the steps in the data mining process?

The topic has to be thoroughly understood and work has to be performed accordingly
Value select the data set you have to be very careful about its quality
Extracting beneficial and relevant data is the major aim of choosing any data set
You need to prepare and process the data after extracting it
Data modeling and remodelling based on the user requirement is the fourth step
Understanding all data aspects are very important for analyzing the presence of leakage and fault in the data processing
As the evaluation is completed data can be used for analyzing and other purposes

In all these steps, data mining standards, algorithms, and models play a very significant role. You can get complete informative and analytical support from our technical experts’ team at any time regarding your data mining thesis. You can always feel free to contact us for any kind of support for your thesis topics in data mining. What are the four major stages of the data mining process? Chronologically the stages of data mining include the following

Collection of data
Dimensionality reduction (PCA and SVD)
Measurement of distance
Prediction (data classification – ANN, SVM, KNN, Rules, Decision Trees and Bayesian networks)
Clustering (hierarchical, density, k means, and message passing)
Association rule mining
Data interpretation

Since our experts have more than two decades of experience in data mining research, you can surely get all your queries resolved with our support. The customized research supports that we provide include practical explanations and demonstrations with complete technical notes and descriptions. We ensure to render confidential research and thesis writing support for all thesis topics in data mining. Get in touch with us for reliable and high-quality data mining research guidance. Let us now talk about the skills and qualifications needed for the successful implementation of data mining projects

What kind of skills are required for a data mining project?

Analysing data to provide supportive points to both true and false facts
Since the process of data evolution seems to be a slow process, human data analysis skills remain the same, provided that all the other factors are constant
Deployment of faster hardware which includes even the Quantum computing
The skill to analyze huge amount of data which are collected autonomously is very important
Betterment and accessibility of open source software is also required for better data analysis and mining

With the help of our technical experts, qualified engineers, and experienced data analysts, you can surely develop and establish all the above-required skills effectively. The standard books and benchmark references that we provide can enable you to choose the best thesis topics in data mining. In this regard let us have a look into the major and recent data mining thesis topics below

It is a method of designing manufacturing techniques ahead of time, determining the extraction path of every single item component or assemblage, and arranging, beginning, and ending for each important basis and setup.
As a result, we could have balanced storage of resources and stable manufacturing utilizing data mining tools.
Internet platforms have varying and data set conceptual frameworks for managing depth of subject knowledge and associated data sets
These datasets contain the same parameters and phenomena that occur in many records, enabling prior records to also be built on different data sets.
Instead of analyses and collections that hinder anyone else from developing on top of the completed project, investigations must be supplied as original data in a consistent format using matlab simulation .
Scalable visualization as well as modeling platforms that enable the user to filter and modify data, explore hypotheses, provide findings, and reduce the time taken to convert records into a version that can be published.
One might take the knowledge through prior experiments or test cases and use it to operate more effectively through data mining methods.
We can reduce the number of errors by referring to previous missteps and applying what we’ve learned to get good outcomes.
Researchers can identify fraudsters by using a bigdata mapreduce approach
It is primarily done by collecting even more relevant data about a particular architecture in the way of knowing and then analyzing them to see if they are legitimate or not.

Currently, we are offering thesis writing guidance with proper grammatical checks, internal review, and multiple revisions. So you can completely depend on us for your data mining thesis. Altogether, a master’s thesis presents study evidence to validate a graduate pupil’s research and technical requirements for a credential. Although some graduates provide non-thesis master’s degree options, the thesis seems to be the standard capstone requirement for many here. So now you understand what a thesis is, you can determine if it’s a good alternative for your profession or if a detailed assessment is a preferred idea.

How long is a thesis for a master’s?

The master’s thesis can range anywhere between one hundred and three hundred pages long, not counting the bibliography.
The quantity will be determined by several criteria, which include the topic and research approach.
There is no such thing as a “proper” length of the page
Rather, the thesis ought to be sufficient enough to clearly and concisely present all important facts.

This tendency, we anticipate, would facilitate and encourage people to invest additional time refining insights rather than gathering, purifying, and otherwise organizing the data that they require. For any further clarifications related to thesis topics in data mining, we insist you check out our website or directly get in touch with us. Our experts are always happy to support you.

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PhD Research Topics in Data Mining

In recent times, there is a massive growth in information generation through “IoT.” At the same time, it stores in “Cloud Computing.” PhD Research Topics in Data Mining is the academic stock of hot topics. It intends to convert our line of thoughts to your research As a result, it ‘ opens the way for research in data mining.’ Hence, join us to put your career on the right track of data mining. So that you will get ‘thrice times better success in your PhD.’

SOUNDFUL TOPICS

DNA and also quantum computing for data mining
Spatial data mining
Graph theory for information retrieval
Semantic web mining
Multimedia retrieval
Personalized recommender systems
Data warehousing integration
Mining from low-quality sources
Database management for information storage
Context-aware computing and also in content-based retrieval
Low-quality audio mining
Multimedia quality assessment
Social network sentiment analysis
P2P and grid databases management
Data mining for IoT applications
MapReduce optimization for itemset mining

Our tireless pros from PhD Research Topics in Data Mining will uplift your research through their energetic ideas. On the whole, we are here to polish each nook of your research . For this reason, we also work on apt selection of simulation tools, datasets, and journals .

DATASETS FOR IDS

ISCXIDS2012

PhD Research Topics in data Mining

Be Smart and Go With Our PhD Research Topics in Data Mining On the road to Huge Success!!!

Analysis of Large-Scale Spatio-Temporal Data using Progressive Partition and Multidimensional Pattern Extraction

Recursive Event Sequence Exploration using Interweaving Queries and Pattern Mining

An Effective Minimum Spanning Tree Clustering for Anti-Noise Process Mining Algorithm

Visual Analytics of Scientific Data Sets using Graph-Based Techniques

An Analysis of Data Flow and Visualization for Spatiotemporal Statistical Data without Trajectory Information

Multimodal Data Correlation for Device Clustering Algorithm in Cognitive Internet of Things

Improved STRAP –Based Dynamic Clustering Scheme for Evolving Data Streams

Distributed storage system for electric power data using Hbase

Itemset Mining Methods for Detection of Frequent Alarm Patterns in Industrial Alarm Floods

An Efficient Algorithm for Clustering Categorical Data With Set-Valued Features

A Privacy Preserving in Multi-Access Edge Computing for Heterogeneous IoT over Big Data

Hidden Temporal Information and Rule-Based Entity Resolution on Database

A Automatic Fault Diagnosis and Prognosis for Distribution Automation using Data Analytic Methodology

Leveraging Graph Mining based on Compression for Behavior-Based Malware Detection

An Efficient IoT Enabled Parallel Mining Algorithm Representative Pattern Set of Large-Scale Itemsets

Cluster-Aided Wireless Channel Modeling based on Big Data Algorithms

IoT Enabled Three Hierarchical Levels of Big-Data Market Model in Multiple Data Sources

A Methodology to discovering companion patterns using traffic data stream

A Clustering based on Uncertain Data in Distributed Peer-to-Peer Networks

Grammar-Based Genetic Programming for Mining Context-Aware Association Rules

MILESTONE 1: Research Proposal

Finalize journal (indexing).

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16 Data Mining Projects Ideas & Topics For Beginners [2024]

Introduction

A career in Data Science necessitates hands-on experience, and what better way to obtain it than by working on real-world data mining projects? This post provides a wide range of data mining project ideas for beginners. Whether you’re looking at data mining in database management systems, data mining projects in Java, or creative data mining project ideas, this list has you covered.

Today, data mining has become strategically important to organizations across industries. It not only helps in predicting outcomes and trends but also in removing bottlenecks and improving existing processes. Data mining research topics 2020 was already in the search bar of millions of users 2 years ago . It looks like this trend is about to continue in 2024 and beyond. So, if you are a beginner, the best thing you can do is work on some real-time data mining projects.

If you are just getting started in data science, making sense of advanced data mining techniques can seem daunting. Along with the plethora of data mining research topics available online , we have compiled some useful data mining project topics to support you in your learning journey.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment if you do not work on data mining projects yourself . In this article, we will be exploring some fun and exciting data mining projects and data mining research topics which beginners can work on to put their data mining knowledge to test. In this post, you will learn about top 16 data mining projects for beginners.

In this article, you will find 42 top python project ideas for beginners to get hands-on experience on Python

But first, let’s address the more important and frequently question that must be lurking in your mind: why to build data mining projects?

But before we begin, let us look at an example to decode what data mining is all about. Suppose you have a data set containing login logs of a web application. It can include things like the username, login timestamp, activities performed, time spent on the site before logging out, etc.

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Such unstructured data in itself would not serve any purpose unless it is organized systematically and analyzed to extract relevant information for the business. By applying the different techniques of data mining, you can discover user habits, preferences, peak usage timings, etc. These insights can further increase the software system’s efficiency and boost its user-friendliness. Learn more about data mining with our data science programs.

In today’s digital era, the computing processes of collecting, cleaning, analyzing, and interpreting data make up an integral part of business strategies. So, data scientists are required to have adequate knowledge of methods like pattern tracking, classification, cluster analysis, prediction, neural networks, etc. The more you experiment with different data mining projects, the more knowledge you gain.

Data Mining Project Ideas & Topics for Beginners

This list of data mining projects for students is suited for beginners, and those just starting out with Data Science in general. These data mining projects will get you going with all the practicalities you need to succeed in your career.

Further, if you’re looking for data mining project for final year, this list should get you going as this list also contains data mining projects for students . So, without further ado, let’s jump straight into some data mining projects that will strengthen your base and allow you to climb up the ladder.

1. iBCM: interesting Behavioral Constraint Miner

One of the best ideas to start experimenting you hands-on data mining projects for students is working on iBCM. A sequence classification problem deals with the prediction of sequential patterns in data sets. It discovers the underlying order in the database based on specific labels. In doing so, it applies the simple mathematical tool of partial orders. However, you would require a better representation to achieve more accurate, concise, and scalable classification. And a sequence classification technique with a behavioral constraint template can address this need.

With the iBCM project, you can delve into the field of sequence categorization. Using behavioral constraint templates, this venture predicts sequential patterns inside datasets. This method employs mathematical tools such as partial orders to reveal underlying data patterns in an accurate and simple manner. Beyond traditional sequence mining, iBCM finds a wide range of patterns, making it a good starting point for inexperienced data miners.

The interesting Behavioral Constraint Miner (iBCM) project can express a variety of patterns over a sequence, such as simple occurrence, looping, and position-based behavior. It can also mine negative information, i.e., the absence of a particular behavior. So, the iBCM approach goes much beyond the typical sequence mining representations and is a perfect starting point for those looking for data mining projects for students.

2. GERF: Group Event Recommendation Framework

This is one of the simple data mining projects yet an exciting one. It is an intelligent solution for recommending social events, such as exhibitions, book launches, concerts, etc. A majority of the research focuses on suggesting upcoming attractions to individuals. So, a Group Event Recommendation Framework (GERF) was developed to propose events to a group of users.

GERF addresses group social event recommendations by utilizing learning-to-rank algorithms for reliable choices. This project provides efficient event recommendations for a varied user population by extracting group preferences and environmental impacts, with applications ranging from exhibitions to travel services.

This model uses a learning-to-rank algorithm to extract group preferences and can incorporate additional contextual influences with ease, accuracy, and time-efficiency.

Learning to rank, also known as machine-learned ranking (MLR), is the process of building ranking models for systems needing information retrieval using machine learning techniques such as supervised learning, semi-supervised learning, and reinforcement learning.

The objects used for training are organized into lists, with the relative order between the lists being partially described. In most cases, a number or ordinal score is assigned to each item, or a binary judgment (such as “relevant” for true values(binary 1) or “not relevant” for false values(binary 0)) is made.

The objective of the ranking model is to apply the same logic used to rank the training data to the rating of fresh, unknown lists.

Also, it can be conveniently applied to other group recommendation scenarios like location-based travel services.

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3. Efficient similarity search for dynamic data streams

Online applications use similarity search systems for tasks like pattern recognition, recommendations, plagiarism detection, etc. Typically, the algorithm answers nearest-neighbor queries with the Location-Sensitive Hashing or LSH approach, a min-hashing related method. It can be implemented in several computational models with large data sets, including MapReduce architecture and streaming. Mentioning data mining projects can help your resume look much more interesting than others.

For a variety of functions, online apps rely on similarity search engines. This research focuses on effective similarity search strategies for dynamic data streams, with a special emphasis on scalability in huge datasets. Its novel features, such as the use of the Jaccard index as a similarity measure and estimating techniques based on sketching, improve accuracy in pattern recognition and recommendation tasks.

Dynamic data streams, however, require scalable LSH-based filtering and design. To this end, the efficient similarity search project outperforms previous algorithms. Here are some of its main features:

Relies on the Jaccard index as a similarity measure
Suggests a nearest-neighbor data structure feasible for dynamic data streams
Proposes a sketching algorithm for similarity estimation

4. Frequent pattern mining on uncertain graphs

Application domains like bioinformatics, social networks, and privacy enforcement often encounter uncertainty due to the presence of interrelated, real-life data archives. This uncertainty permeates the graph data as well.

Frequent pattern mining on uncertain graphs is critical in settings requiring uncertain data, such as bioinformatics and social networks. This project addresses the issue of transitive interactions with uncertain graph data. It efficiently manages real-world data archives with increased performance by utilizing enumeration-evaluation methods and approximation techniques.

This problem calls for innovative data mining projects that can catch the transitive interactions between graph nodes. This beginner-level data mining projects will help build a strong foundation for fundamental programming concepts. One such technique is the frequent subgraph and pattern mining on a single uncertain graph. The solution is presented in the following format:

An enumeration-evaluation algorithm to support computation under probabilistic semantics
An approximation algorithm to enable efficient problem-solving
Computation sharing techniques to drive mining performance
Integration of check-point based and pruning approaches to extend the algorithm to expected semantics

5. Cleaning data with forbidden itemsets or FBIs

Data cleaning methods typically involve taking away data errors and systematically fixing the issue by specifying constraints (illegal values, domain restrictions, logical rules, etc.)

Data cleansing frequently entails defining limitations to correct inaccuracies. The FBI’s effort introduces a fixing method based on banned itemset, finding constraints in dirty data automatically and improving error detection precision. Empirical evaluations establish the mechanism’s trustworthiness and dependability, which is critical in the big data scenario.

In the real-life big data universe, we are inundated with dirty data that comes without any known constraints. In such a scenario, the algorithm automatically discovers constraints on the dirty data and further uses them to identify and repair errors. But when this discovery algorithm runs on the repaired data again, it introduces new constraint violations, rendering the data erroneous. This is one of the excellent data mining projects for beginners.

Hence, a repairing method based on forbidden itemsets (FBIs) was devised to record unlikely co-occurrences of values and detect errors with more precision. And empirical evaluations establish the credibility and reliability of this mechanism.

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6. Protecting user data in profile-matching social networks

This is one of the convenient data mining projects that has a lot of use in the future. Consider the user profile database maintained by the providers of social networking services, such as online dating sites. The querying users specify certain criteria based on which their profiles are matched with that of other users. This process has to be secure enough to protect against any kind of data breaches. There are some solutions in the market today that use homomorphic encryption and multiple servers for matching user profiles to preserve user privacy.

Read our popular Data Science Articles

7. PrivRank for social media

Social media sites mine their users’ preferences from their online activities to offer personalized recommendations. However, user activity data contains information which can be used to infer private details about an individual (for example, gender, age, etc.) And any leak or release of such user-specified data can increase the risk of interference attacks.

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8. Practical PEKs scheme over encrypted email in cloud server

In the light of current high-profile public events related to email leaks, the security of such sensitive messages has emerged as a primary concern for users worldwide. To that end, the Public Encryption with Keyword Search (PEKS) technology offers a viable solution. This is one of the useful data mining projects in which this combines security protection with efficient search operability functions.

When searching over a sizable encrypted email database in a cloud server, we would want the email receivers to perform quick multi-keyword and boolean searches without revealing additional information to the server.

Read: Data Mining Real World Applications

9. Sentimental analysis and opinion mining for mobile networks

This project concerns post-publishing applications where a registered user can share text posts or images and also leave comments on posts. Under the prevailing system, users have to go through all the comments manually to filter out verified comments, positive comments, negative remarks, and so on.

With the sentiment analysis and opinion mining system, users can check the status of their post without dedicating much time and effort. It provides an opinion on the comments made on a post and also gives the option to view a graph.

10. Mining the k most frequent negative patterns via learning

In behavior informatics, the negative sequential patterns (NSPs) can be more revealing than the positive sequential patterns (PSPs) . For instance, in a disease or illness-related study, data on missing a medical treatment can be more useful than data on attending a medical procedure. But to the present day, NSP mining is still at a nascent stage. And the ‘Topk-NSP+’ algorithm presents a reliable solution for overcoming the obstacles in the current mining landscape. This is one of the trending data mining and this is how the project proposes the algorithm:

Mining the top-k PSPs with the existing method
Mining the to-k NSPs from these PSPs by using an idea similar to the top-k PSPs mining
Employing three optimization strategies to select useful NSPs and reduce computational costs

Also try: Machine Learning Project Ideas for Beginners

11. Automated personality classification project

The automatic system analyzes the characteristics and behaviors of participants. And after observing the past patterns of data classification, it predicts a personality type and stores its own patterns in a dataset. This project idea can be summarized as follows:

Store personality-related data in a database
Collect associated characteristics for each user
Extract relevant features from the text entered by the participant
Examine and display the personality traits
Interlink personality and user behavior (There can be varying degrees of behavior for a particular personality type)

Such models are commonplace in career guidance services where a student’s personality is matched with suitable career paths. This can be an interesting and useful data mining projects.

12. Social-Aware social influence modeling

This is one of the most popular data mining mini projects. This project deals with big social data and leverages deep learning for sequential modeling of user interests. The stepwise process is described below:

A preliminary analysis of two real datasets (Yelp and Epinions)
Discovery of statistically sequential actions of users and their social circles, including temporal autocorrelation and social influence on decision-making
Presentation of a novel deep learning model called Social-Aware Long Short-Term Memory (SA-LSTM), which can predict the type of items or Points of Interest that a particular user will buy or visit next. Long short-term memory, often known as LSTM, is a kind of neural network that is used in the domains of deep learning and artificial intelligence. LSTM neural networks have feedback connections, in contrast to more traditional feedforward neural networks so that they can change the training parameters or hyperparameters to be more precise, with each epoch. LSTM is a kind of recurrent neural network, commonly known as an RNN, which is capable of processing, not just individual data points but also complete data sequences.

Experimental results reveal that the structure of this proposed solution enables higher prediction accuracy as compared to other baseline methods.

This is one of the data mining mini projects that will definitely help you get some real-world exposure.

13. Predicting consumption patterns with a mixture approach

Individuals consume a large selection of items in the digital world today. For example, while making purchases online, listening to music, using online navigation, or exploring virtual environments. Applications in these contexts employ predictive modeling techniques to recommend new items to users. However, in many situations, we want to know the additional details of previously-consumed items and past user behavior. And this is where the baseline approach of matrix factorization-based prediction falls short. This is one of the creative data mining projects.

A mixture model with repeated and novel events offers a suitable alternative for such problems. It aims to deliver accurate consumption predictions by balancing individual preferences in terms of exploration and exploitation. Also, it is one of those data mining project topics that include an experimental analysis using real-world datasets. The study’s results show that the new approach works efficiently across different settings, from social media and music listening to location-based data.

14. GMC: Graph-based Multi-view Clustering

The existing clustering methods for multi-view data require an extra step to produce the final cluster as they do not pay much attention to the weights of different views. Moreover, they function on fixed graph similarity matrices of all views. And this is the perfect idea for your next data mining project as this can also be considered as a graph mining projects .

A novel Graph-based Multi-view Clustering (GMC) can tackle this issue and deliver better results than the previous alternatives. It is a fusion technique that weights data graph matrices for all views and derives a unified matrix, directly generating the final clusters. Other features of the graph mining projects include:

Partition of data points into the desired number of clusters without using a tuning parameter. For this, a rank constraint is imposed on the Laplacian matrix of the unified matrix.
Optimization of the objective function with an iterative optimization algorithm

15. ITS: Intelligent Transportation System

A multi-purpose traffic solution generally aims to ensure the following aspects:

Transport service’s efficiency
Transport safety
Reduction in traffic congestion
Forecast of potential passengers
Adequate allocation of resources

Consider a project that uses the above system to optimize the process of bus scheduling in a city. ITS is one of the interesting data mining projects for beginners. You can take the past three years’ data from a renowned bus service company, and apply uni-variate multi-linear regression to conduct passengers’ forecasts.

Further, you can calculate the minimum number of buses required for optimization in a Generic Algorithm. Finally, you validate your results using statistical techniques like mean absolute percentage error (MAPE) and mean absolute deviation (MAD). Mean Absolute Percentage Error(MAPE): The accuracy of a forecasting system may be quantified by calculating the mean absolute percentage error (MAPE). Measured as a percentage, it is derived by taking the sum of the absolute values of the errors across all time periods and dividing by the real values to provide a reading on how close the estimate is to the true value.

The most popular way to quantify forecast errors is via the use of the mean absolute percentage error (MAPE), perhaps because the variable’s units are already in percentage form. A lack of extremes in the data is necessary for optimal performance (and no zeros). In regression analysis and model assessment, it is frequently used as a loss function.

Mean Absolute Deviation(MAD): It measures how far each data point is from the dataset’s mean value. It helps us get a sense of the data’s overall dispersion. To find out the MAD for a data set, we must first calculate the mean and then the distance of each data point from the mean using MPD(Mean positive distances) which would yield the absolute deviation.

This absolute deviation is the measure of this gap between the mean and each data point. Now, we take the total of all these deviations, add it and then divide it by the total number of data points in the data set.

16. TourSense for city tourism

City-scale transport data about buses, subways, etc. could also be used for tourist identification and preference analytics. But relying on traditional data sources, such as surveys and social media, can result in inadequate coverage and information delay.

The TourSense project demonstrates how to override such shortcomings and provide more valuable insights. This tool would be useful for a wide range of stakeholders, from transport operators and tour agencies to tourists themselves. This is one of the excellent data mining projects for beginners. Here are the main steps involved in its design:

A graph-based iterative propagation learning algorithm to identify tourists from other public commuters
A tourist preference analytics model (utilizing the tourists’ trace data) to learn and predict their next tour
An interactive UI to serve easy information access from the analytics

Data Mining Projects: Conclusion

In this article, we have covered 16 data mining projects. If you wish to improve your data mining skills, you need to get your hands on these data mining projects.

Dive into Data Science involves more than just academic understanding; it also necessitates practical experience. These data mining project ideas are designed for novices, with options to investigate sequence classification, group suggestions, similarity search, graph mining, and data cleaning. As you work on these projects, you’ll lay a solid foundation in Data Science and prepare for future challenges in this ever-changing area.

Data mining and correlated fields have experienced a surge in hiring demand in the last few years as data mining research topics 2020 was already in the search bar of millions of users 2 years ago and is still there . With the above data mining project topics, you can keep up with the market trends and developments. So, stay curious and keep updating your knowledge!

If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

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Frequently Asked Questions (FAQs)

As the name suggests, data mining refers to the process of mining or extraction of patterns from large data sets. The methods it involves include the combined knowledge of machine learning, statistics, and database systems. Before applying data mining techniques, you need to assemble a large dataset that must be large enough to contain patterns to be mined. There are 6 prominent steps that are involved in the data mining process. These steps are anomaly detection, association rule learning, clustering, classification, regression, and summarization.

Classification in data mining allows enterprises to arrange large sets of data according to the target categories. Once ordered in this manner, the enterprises could see the data clearly and analyze the risks and profits easily which in turn helps the businesses to grow. Classification can also be understood as a way to generalize known structures to apply to new data. The analysis is based on several patterns that are found in the data. These patterns help to sort the data into different groups.

Projects are all about experimenting and testing your skills. They let you use all of your creativity and develop a useful product out of it. Building data mining projects will not only give you hands-on experience but will also enhance your knowledge pool. You can add these amazing projects to your resume to showcase your skills to potential employers. These projects will help you to implement your theoretical knowledge into action and gain practical benefits from it.

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Data Mining Projects

Data mining projects for engineers researchers and enthusiasts. Get the widest list of data mining based project titles as per your needs. These systems have been developed to help in research and development on information mining systems. Get ieee based as well as non ieee based projects on data mining for educational needs. Nevonprojects has a directory of latest and innovative data mining project ideas for students and researchers. We provide data mining projects with source code for studies and research. These systems are proposed to help as applications that will help to solve many real time issues on various software based systems. Due to a large accommodation of data collected online these data mining algorithms are used to extract desired data within the least time frame for best use of the data. Now browse through our list of data mining projects and select your desired topics below.

Need Help Selecting a Topic ?

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AI Healthcare Bot System using Python
Chronic Obstructive Pulmonary Disease Prediction System
College Placement System Using Python
Face Recognition Attendance System for Employees using Python
Liver Cirrhosis Prediction System using Random Forest
Multiple Disease Prediction System using Machine Learning
Secure Persona Prediction and Data Leakage Prevention System using Python
Stroke Prediction System using Linear Regression
Toxic Comment Classification System using Deep Learning
Movie Success Prediction System using Python
Speech Emotion Detection System using Python
Student Feedback Review System using Python
Music Genres Classification using KNN System
Traffic Sign Recognition System using CNN
Face Recognition Attendance System using Python
Pneumonia Detection using Chest X-Ray
Parkinson’s Detector System using Python
Cryptocurrency price prediction using Machine Learning Python
Depression Detection System using Python
Car Lane Detection Using NumPy OpenCV Python
Sign Language Recognition Using Python
Signature verification System using Python
Predicting House Price Using Decision Tree
Blockchain Based Antiques Verification System
Brain Tumor and Alzheimer’s Detection Flutter App
Text Translation App Using Google API
AI-Based Picture Translation App
Mental Health Check app using NLP Flutter
Patient Data Management System using Blockchain
Loyalty Points Exchange System using Blockchain
Android Heart Disease Prediction App
Knee Osteoarthritis Detection & Severity Prediction
Online Fake Logo Detection System
Doctor Appointment & Disease Prediction App
Android College Connect Chat App
Tour Recommender App Using Collaborative Filtering
Voice based Intelligent Virtual Assistance for Windows
Smart Health Disease Prediction Using Naive Bayes
Chat Bot for Granite Online Ecommerce Shop
Predictive Analysis of Digital Agriculture
Food Recipes Rating System based on Emotional Analysis
Artificial Intelligence HealthCare Chatbot System
Online Assignment Plagiarism Checker Project using Data Mining
Teachers Automatic Time-Table Software Generation System using PHP
Online Examination System Project in ASP.Net
Online book recommendation system using Collaborative filtering
Diabetes Prediction Using Data Mining
Data Mining for Sales Prediction in Tourism Industry
Higher Education Access Prediction Software
Hotel Recommendation System Based on Hybrid Recommendation Model
Detecting Fraud Apps Using Sentiment Analysis
Personality Prediction System Through CV Analysis
TV Show Popularity Analysis Using Data Mining
Twitter Trend Analysis Using Latent Dirichlet Allocation
Your Personal Nutritionist Using FatSecret API
Secure E Learning Using Data Mining Techniques
Price Negotiator Ecommerce ChatBot System
Predicting User Behavior Through Sessions Web Mining
Online Book Recommendation Using Collaborative Filtering
Movie Success Prediction Using Data Mining Php
Monitoring Suspicious Discussions On Online Forums Php
Fake Product Review Monitoring & Removal For Genuine Ratings Php
Detecting E Banking Phishing Using Associative Classification
A Commodity Search System For Online Shopping Using Web Mining
Detecting Phishing Websites Using Machine Learning
Student Information Chatbot Project
Website Evaluation Using Opinion Mining
Filtering political sentiment in social media from textual information
Evaluation of Academic Performance of Students with Fuzzy Logic
Document Sentiment Analysis Using Opinion Mining
Crime Rate Prediction Using K Means
Cooking Recipe Rating Based On Sentiment Analysis
Social Media Community Using Optimized Clustering Algorithm
Online user Behavior Analysis On Graphical Model
Student Grade Prediction Using C4.5 Decision Tree
Cancer Prediction Using Data Mining
Symptom Based Clinical Document Clustering by Matrix Factorization
Using Data Mining To Improve Consumer Retailer Connectivity
Financial Status Analysis Using Credit Score Rating
E Banking Log System
Stream Analysis For Career Choice Aptitude Tests
Product Review Analysis For Genuine Rating
Periodic Census With Graphical Representation
Android Smart City Traveler
Heart Disease Prediction Project
Content Summary Generation Using NLP
Monitoring Suspicious Discussions On Online Forums Using Data Mining
Opinion Mining For Social Networking Site
Web Content Trust Rating Prediction Using Evidence Theory
Topic Detection Using Keyword Clustering
An Adaptive Social Media Recommendation System
Detecting E Banking Phishing Websites Using Associative Classification
Canteen Automation System
Opinion Mining For Hotel Rating Through Reviews
Employee Performance Evaluation For Top Performers & Recruitment
Data Mining For Improved Customer Relationship Management
Social Network Privacy Using Two Tales Of Privacy Algorithm
Impartial Intrusion & Crime Detection Without Gender or Caste Discrimination
A neuro-fuzzy agent based group decision HR system for candidate ranking
Workload & Resource Consumption Analysis For Online Travel & Booking Site
Performance Evaluation in Virtual Organizations Using Data Mining & Opinion Mining
E Commerce Product Rating Based On Customer Review Mining
Weather Forecasting Using Data Mining
Unique User Identification Across Multiple Social Networks
Opinion Mining For Restaurant Reviews
Sentiment Analysis for Product Rating
Opinion Mining For Comment Sentiment Analysis
Movie Success Prediction Using Data Mining
Fake Product Review Monitoring And Removal For Genuine Online Product Reviews Using Opinion Mining
Biomedical Data Mining For Web Page Relevance Checking
Data Mining For Automated Personality Classification
Web Data Mining To Detect Online Spread Of Terrorism
Real Estate Search Based On Data Mining
College Enquiry Chat Bot
Bikers Portal
Smart Health Prediction Using Data Mining
Image Mining Project
Advanced Reliable Real Estate Portal
User Web Access Records Mining For Business Intelligence
Mobile(location based) Advertisement System
Smart Health Consulting Project
Sentiment Based Movie Rating System
Question paper generator system
Seo optimizer and suggester
Banking Bot Project
Web Mining For Suspicious Keyword Prominence
Customer Behaviour Prediction Using Web Usage Mining
Stock Market Analysis and Prediction

Need Custom Made Web Based Project / System ?

This list of data mining project topics has been complied to help students and researchers to get a jump start in their electronics development. Our developers constantly compile latest data mining project ideas and topics to help student learn more about data mining algorithms and their usage in the software industry. Since data mining algorithms can be used for a wide variety of purposes from behavior prediction to suspicious activity detection our list of data mining projects keeps on expanding every week with some new ideas for your research.

Home > Statler College of Engineering and Mineral Resources > MININGENG > Mining Engineering Graduate Theses and Dissertations

Mining Engineering Graduate Theses and Dissertations

Theses/dissertations from 2024 2024.

CHARACTERIZATION AND EVALUATION OF VARIOUS BIOCHAR TYPES AS GREEN ADSORBENTS FOR RARE EARTH ELEMENT RECOVERY FROM AQUEOUS SOLUTIONS , Oluwaseun Victor Famobuwa

Selective Recovery of Various Critical Metals from Acid Mine Drainage Sludge , Gorkem Gecimli

Theses/Dissertations from 2023 2023

Development of A Hydrometallurgical Process for the Extraction of Cobalt, Manganese, and Nickel from Acid Mine Drainage Treatment Byproduct , Alejandro Agudelo Mira

Selective Recovery of Rare Earth Elements from Acid Mine Drainage Treatment Byproduct , Zeynep Cicek

Identification of Rockmass Deformation and Lithological Changes in Underground Mines by Using Slam-Based Lidar Technology , Francisco Eduardo Gil Hurtado

Analysis of the Brittle Failure Mechanism of Underground Stone Mine Pillars by Implementing Numerical Modeling in FLAC3D , Rosbel Jimenez

Analysis of the root causes of fatal injuries in the United States surface mines between 2008 and 2021. , Maria Fernanda Quintero

AUGMENTED REALITY AND MOBILE SYSTEMS FOR HEAVY EQUIPMENT OPERATORS IN SURFACE MINING , Juan David Valencia Quiceno

Theses/Dissertations from 2022 2022

Integrated Large Discontinuity Factor, Lamodel and Stability Mapping Approach for Stone Mine Pillar Stability , Mustafa Baris Ates

Noise Exposure Trends Among Violating Coal Mines, 2000 to 2021 , Hanna Grace Davis

Calcite depression in bastnaesite-calcite flotation system using organic acids , Emmy Muhoza

Investigation of Geomechanical Behavior of Laminated Rock Mass Through Experimental and Numerical Approach , Qingwen Shi

Static Liquefaction in Tailing Dams , Jose Raul Zela Concha

Experimental and Theoretical Investigation on the Initiation Mechanism of Low-Rank Coal's Self-Heating Process , Yinan Zhang

Development of an Entry-Scale Modeling Methodology to Provide Ground Reaction Curves for Longwall Gateroad Support Evaluation , Haochen Zhao

Size effect and anisotropy on the strength of shale under compressive stress conditions , Yun Zhao

Theses/Dissertations from 2021 2021

Evaluation of LIDAR systems for rock mass discontinuity identification in underground stone mines from 3D point cloud data , Mario Alejandro Bendezu de la Cruz

Implementing the Empirical Stone Mine Pillar Strength Equation into the Boundary Element Method Software LaModel , Samuel Escobar

Recovery of Phosphorus from Florida Phosphatic Waste Clay , Amir Eskanlou

Optimization of Operating Conditions and Design Parameters on Coal Ultra-Fine Grinding Through Kinetic Stirred Mill Tests and Numerical Modeling , Francisco Patino

The Effect of Natural Fractures on the Mechanical Behavior of Limestone Pillars: A Synthetic Rock Mass Approach Application , Mustafa Can Süner

Evaluation of Various Separation Techniques for the Removal of Actinides from A Rare Earth-Containing Solution Generated from Coarse Coal Refuse , Deniz Talan

Geology Oriented Loading Approach for Underground Coal Mines , Deniz Tuncay

Various Operational Aspects of the Extraction of Critical Minerals from Acid Mine Drainage and Its Treatment By-product , Zhongqing Xiao

Theses/Dissertations from 2020 2020

Adaptation of Coal Mine Floor Rating (CMFR) to Eastern U.S. Coal Mines , Sena Cicek

Upstream Tailings Dam - Liquefaction , Mladen Dragic

Development, Analysis and Case Studies of Impact Resistant Steel Sets for Underground Roof Fall Rehabilitation , Dakota D. Faulkner

The influence of spatial variance on rock strength and mechanism of failure , Danqing Gao

Fundamental Studies on the Recovery of Rare Earth Elements from Acid Mine Drainage , Xue Huang

Rational drilling control parameters to reduce respirable dust during roof bolting operations , Hua Jiang

Solutions to Some Mine Subsidence Research Challenges , Jian Yang

An Interactive Mobile Equipment Task-Training with Virtual Reality , Lazar Zujovic

Theses/Dissertations from 2019 2019

Fundamental Mechanism of Time Dependent Failure in Shale , Neel Gupta

A Critical Assessment on the Resources and Extraction of Rare Earth Elements from Acid Mine Drainage , Christopher R. Vass

Time-dependent deformation and associated failure of roof in underground mines , Yuting Xue

Theses/Dissertations from 2018 2018

Parametric Study of Coal Liberation Behavior Using Silica Grinding Media , Adewale Wasiu Adeniji

Three-dimensional Numerical Modeling Encompassing the Stability of a Vertical Gas Well Subjected to Longwall Mining Operation - A Case Study , Bonaventura Alves Mangu Bali

Shale Characterization and Size-effect study using Scanning Electron Microscopy and X-Ray Diffraction , Debashis Das

Behaviour Of Laminated Roof Under High Horizontal Stress , Prasoon Garg

Theses/Dissertations from 2017 2017

Optimization of Mineral Processing Circuit Design under Uncertainty , Seyed Hassan Amini

Evaluation of Ultrasonic Velocity Tests to Characterize Extraterrestrial Rock Masses , Thomas W. Edge II

A Photogrammetry Program for Physical Modeling of Subsurface Subsidence Process , Yujia Lian

An Area-Based Calculation of the Analysis of Roof Bolt Systems (ARBS) , Aanand Nandula

Developing and implementing new algorithms into the LaModel program for numerical analysis of multiple seam interactions , Mehdi Rajaeebaygi

Adapting Roof Support Methods for Anchoring Satellites on Asteroids , Grant B. Speer

Simulation of Venturi Tube Design for Column Flotation Using Computational Fluid Dynamics , Wan Wang

Theses/Dissertations from 2016 2016

Critical Analysis of Longwall Ventilation Systems and Removal of Methane , Robert B. Krog

Implementing the Local Mine Stiffness Calculation in LaModel , Kaifang Li

Development of Emission Factors (EFs) Model for Coal Train Loading Operations , Bisleshana Brahma Prakash

Nondestructive Methods to Characterize Rock Mechanical Properties at Low-Temperature: Applications for Asteroid Capture Technologies , Kara A. Savage

Mineral Asset Valuation Under Economic Uncertainty: A Complex System for Operational Flexibility , Marcell B. B. Silveira

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges , Christopher R. Vass

Spontaneous Combustion of South American Coal , Brunno C. C. Vieira

Calibrating LaModel for Subsidence , Jian Yang

Theses/Dissertations from 2015 2015

Coal Quality Management Model for a Dome Storage (DS-CQMM) , Manuel Alejandro Badani Prado

Design Programs for Highwall Mining Operations , Ming Fan

Development of Drilling Control Technology to Reduce Drilling Noise during Roof Bolting Operations , Mingming Li

The Online LaModel User's & Training Manual Development & Testing , Christopher R. Newman

How to mitigate coal mine bumps through understanding the violent failure of coal specimens , Gamal Rashed

Theses/Dissertations from 2014 2014

Effect of biaxial and triaxial stresses on coal mine shale rocks , Shrey Arora

Stability Analysis of Bleeder Entries in Underground Coal Mines Using the Displacement-Discontinuity and Finite-Difference Programs , Xu Tang

Experimental and Theoretical Studies of Kinetics and Quality Parameters to Determine Spontaneous Combustion Propensity of U.S. Coals , Xinyang Wang

Bubble Size Effects in Coal Flotation and Phosphate Reverse Flotation using a Pico-nano Bubble Generator , Yu Xiong

Integrating the LaModel and ARMPS Programs (ARMPS-LAM) , Peng Zhang

Theses/Dissertations from 2013 2013

Column Flotation of Subbituminous Coal Using the Blend of Trimethyl Pentanediol Derivatives and Pico-Nano Bubbles , Jinxiang Chen

Applications of Surface and Subsurface Subsidence Theories to Solve Ground Control Problems , Biao Qiu

Calibrating the LaModel Program for Shallow Cover Multiple-Seam Mines , Morgan M. Sears

The Integration of a Coal Mine Emergency Communication Network into Pre-Mine Planning and Development , Mark F. Sindelar

Factors considered for increasing longwall panel width , Jack D. Trackemas

An experimental investigation of the creep behavior of an underground coalmine roof with shale formation , Priyesh Verma

Evaluation of Rope Shovel Operators in Surface Coal Mining Using a Multi-Attribute Decision-Making Model , Ivana M. Vukotic

Theses/Dissertations from 2012 2012

Calculating the Surface Seismic Signal from a Trapped Miner , Adeniyi A. Adebisi

Comprehensive and Integrated Model for Atmospheric Status in Sealed Underground Mine Areas , Jianwei Cheng

Production and Cost Assessment of a Potential Application of Surface Miners in Coal Mining in West Virginia , Timothy A. Nolan

The Integration of Geomorphic Design into West Virginia Surface Mine Reclamation , Alison E. Sears

Truck Cycle and Delay Automated Data Collection System (TCD-ADCS) for Surface Coal Mining , Patricio G. Terrazas Prado

New Abutment Angle Concept for Underground Coal Mining , Ihsan Berk Tulu

Theses/Dissertations from 2011 2011

Experimental analysis of the post-failure behavior of coal and rock under laboratory compression tests , Dachao Neil Nie

The influence of interface friction and w/h ratio on the violence of coal specimen failure , Simon H. Prassetyo

Theses/Dissertations from 2010 2010

A risk management approach to pillar extraction in the Central Appalachian coalfields , Patrick R. Bucks

The Impacts of Longwall Mining on Groundwater Systems -- A Case of Cumberland Mine Panels B5 and B6 , Xinzhi Du

Evaluation of ultrafine spiral concentrators for coal cleaning , Meng Yang

Theses/Dissertations from 2009 2009

Development of a coal reserve GIS model and estimation of the recoverability and extraction costs , Chandrakanth Reddy Apala

Application and evaluation of spiral separators for fine coal cleaning , Zhuping Che

Weak floor stability in the Illinois Basin underground coal mines , Murali M. Gadde

Design of reinforced concrete seals for underground coal mines , Rajagopala Reddy Kallu

Employing laboratory physical modeling to study the radio imaging method (RIM) , Jun Lu

Influence of cutting sequence and time effects on cutters and roof falls in underground coal mine -- numerical approach , Anil Kumar Ray

Implementing energy release rate calculations into the LaModel program , Morgan M. Sears

Modeling PDC cutter rock interaction , Ihsan Berk Tulu

Analytical determination of strain energy for the studies of coal mine bumps , Qiang Xu

Improvement of the mine fire simulation program MFIRE , Lihong Zhou

Theses/Dissertations from 2008 2008

Program-assisted analysis of the transverse pressure capacity of block stoppings for mine ventilation control , Timothy J. Batchler

Analysis of factors affecting wireless communication systems in underground coal mines , David P. McGraw

Analysis of underground coal mine refuge shelters , Mickey D. Mitchell

Theses/Dissertations from 2007 2007

Dolomite flotation of high magnesium phosphate ores using fatty acid soap collectors , Zhengxing Gu

Evaluation of longwall face support hydraulic supply systems , Ted M. Klemetti II

Experimental studies of electromagnetic signals to enhance radio imaging method (RIM) , William D. Monaghan

Analysis of water monitoring data for longwall panels , Joseph R. Zirkle

Theses/Dissertations from 2006 2006

Measurements of the electrical properties of coal measure rocks , Nikolay D. Boykov

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Viridis mining and minerals insiders added au$3.04m of stock to their holdings.

In the last year, multiple insiders have substantially increased their holdings of Viridis Mining and Minerals Limited ( ASX:VMM ) stock, indicating that insiders' optimism about the company's prospects has increased.

Although we don't think shareholders should simply follow insider transactions, we do think it is perfectly logical to keep tabs on what insiders are doing.

View our latest analysis for Viridis Mining and Minerals

Viridis Mining and Minerals Insider Transactions Over The Last Year

In the last twelve months, the biggest single purchase by an insider was when insider Sufian Ahmad bought AU$1.2m worth of shares at a price of AU$0.95 per share. Even though the purchase was made at a significantly lower price than the recent price (AU$1.16), we still think insider buying is a positive. While it does suggest insiders consider the stock undervalued at lower prices, this transaction doesn't tell us much about what they think of current prices.

Viridis Mining and Minerals insiders may have bought shares in the last year, but they didn't sell any. They paid about AU$0.72 on average. We don't deny that it is nice to see insiders buying stock in the company. However, we do note that they were buying at significantly lower prices than today's share price. You can see the insider transactions (by companies and individuals) over the last year depicted in the chart below. If you want to know exactly who sold, for how much, and when, simply click on the graph below!

There are plenty of other companies that have insiders buying up shares. You probably do not want to miss this free list of undervalued small cap companies that insiders are buying.

Viridis Mining and Minerals Insiders Bought Stock Recently

It's good to see that Viridis Mining and Minerals insiders have made notable investments in the company's shares. Not only was there no selling that we can see, but they collectively bought AU$1.1m worth of shares. This is a positive in our book as it implies some confidence.

Insider Ownership Of Viridis Mining and Minerals

Another way to test the alignment between the leaders of a company and other shareholders is to look at how many shares they own. We usually like to see fairly high levels of insider ownership. Insiders own 39% of Viridis Mining and Minerals shares, worth about AU$28m. This level of insider ownership is good but just short of being particularly stand-out. It certainly does suggest a reasonable degree of alignment.

What Might The Insider Transactions At Viridis Mining and Minerals Tell Us?

It is good to see recent purchasing. And the longer term insider transactions also give us confidence. But on the other hand, the company made a loss during the last year, which makes us a little cautious. Given that insiders also own a fair bit of Viridis Mining and Minerals we think they are probably pretty confident of a bright future. So these insider transactions can help us build a thesis about the stock, but it's also worthwhile knowing the risks facing this company. Our analysis shows 4 warning signs for Viridis Mining and Minerals (3 can't be ignored!) and we strongly recommend you look at them before investing.

Of course, you might find a fantastic investment by looking elsewhere. So take a peek at this free list of interesting companies.

For the purposes of this article, insiders are those individuals who report their transactions to the relevant regulatory body. We currently account for open market transactions and private dispositions of direct interests only, but not derivative transactions or indirect interests.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) simplywallst.com. This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email [email protected]

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82 Data Mining Essay Topic Ideas & Examples
Commercial Uses of Data Mining. Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations. A Discussion on the Acceptability of Data Mining.
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Data Mining Dissertation Topics. The term "data mining" refers to an intelligent data lookup capacity that uses statistics-based algorithms and methodologies to find trends, patterns, links, and correlations within the collected data and records. Audio, Pictorial, Video, textual, online, and social media-based mining are only a few examples ...
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Open Topics We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. A non-exhaustive list of open topics is listed below.. If you are interested in a thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email and we will arrange a meeting to talk about the potential ...
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An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks Author: Remmits, Y. L. J. A., 30 Sept 2019 Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach) Student thesis: Master
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Different data mining techniques were applied to the Theseus dataset to build a web application to explore thesis topics and degree programmes using different libraries in Python and JavaScript. Thesis topics were extracted from manually annotated keywords by the authors and curated subjects by the librarians.
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After going through this research based on Data mining, which provide lot of information, you can utilize them to clarify the doubts you have about its technology, applications of this technology, and different topologies of it, algorithms followed by it also about the limitations and how it can be overcome. Data Mining Project Ideas & Topics
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Research Topics in Data Mining offer you nurture platform to shine your research career successfully. Data mining is used to mining meaningful information from large datasets. So we provide support for knowledge mining, i.e., we mine the best and innovative ideas for your project with our top experts' help. We also provide support for all the ...
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Consult the top 50 dissertations / theses for your research on the topic 'Data mining.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
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Applying data mining to telecom churn management. A data mining approach to the prediction of corporate failure. Algorithms and applications for spatial data mining. Mining educational data to analyze students' performance. An attacker's view of distance preserving maps for privacy preserving data mining.
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Truck Cycle and Delay Automated Data Collection System (TCD-ADCS) for Surface Coal Mining, Patricio G. Terrazas Prado. PDF. New Abutment Angle Concept for Underground Coal Mining, Ihsan Berk Tulu. Theses/Dissertations from 2011 PDF. Experimental analysis of the post-failure behavior of coal and rock under laboratory compression tests, Dachao ...
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Viridis Mining and Minerals Insiders Added AU$3.04m Of Stock To Their
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Graph Neural Networks for Spatial Transcriptomics

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Efficient Machine Learning: Pruning, Quantization, Distillation, and More

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Active Learning for Multi Agent 3D Object Detection

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Achieving Long Term Fairness through Curiosity Driven Reinforcement Learning: How intrinsic motivation influences fairness in algorithmic decision making

Activity Recognition Using Deep Learning in Videos under Clinical Setting

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Application of P&ID symbol detection and classification for generation of material take-off documents (MTOs)

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