thesis topic for data mining

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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Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

Implementation of Data Mining Technology in Bonded Warehouse Inbound and Outbound Goods Trade

For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.

Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants

User activity classification and domain-wise ranking through social interactions.

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

A data mining analysis of COVID-19 cases in states of United States of America

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.

Exploring distributed energy generation for sustainable development: A data mining approach

A comprehensive guideline for bengali sentiment annotation.

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

The Influence of E-book Teaching on the Motivation and Effectiveness of Learning Law by Using Data Mining Analysis

This paper studies the motivation of learning law, compares the teaching effectiveness of two different teaching methods, e-book teaching and traditional teaching, and analyses the influence of e-book teaching on the effectiveness of law by using big data analysis. From the perspective of law student psychology, e-book teaching can attract students' attention, stimulate students' interest in learning, deepen knowledge impression while learning, expand knowledge, and ultimately improve the performance of practical assessment. With a small sample size, there may be some deficiencies in the research results' representativeness. To stimulate the learning motivation of law as well as some other theoretical disciplines in colleges and universities has particular referential significance and provides ideas for the reform of teaching mode at colleges and universities. This paper uses a decision tree algorithm in data mining for the analysis and finds out the influencing factors of law students' learning motivation and effectiveness in the learning process from students' perspective.

Intelligent Data Mining based Method for Efficient English Teaching and Cultural Analysis

The emergence of online education helps improving the traditional English teaching quality greatly. However, it only moves the teaching process from offline to online, which does not really change the essence of traditional English teaching. In this work, we mainly study an intelligent English teaching method to further improve the quality of English teaching. Specifically, the random forest is firstly used to analyze and excavate the grammatical and syntactic features of the English text. Then, the decision tree based method is proposed to make a prediction about the English text in terms of its grammar or syntax issues. The evaluation results indicate that the proposed method can effectively improve the accuracy of English grammar or syntax recognition.

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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 person) (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 person) (External coach) & Groen, J. (External person) (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 person) (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 person) (External coach)

Anomaly detection in text data using deep generative models

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

Anomaly detection on vibration data

Supervisor: Hess, S. (Supervisor 1), Pechenizkiy, M. (Supervisor 2), Yakovets, N. (Supervisor 2) & Uusitalo, J. (External person) (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 person) (External coach) & Ya-alimadad, M. (External person) (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 person) (External coach) & Veldsink, J. W. (External person) (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 person) (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 person) (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 person) (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)

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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 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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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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How does data mining work?

A standard data mining design begins with the appropriate business statement in the questionnaire, the appropriate data is collected to tackle it, and the data is prepared for the examination.
What happens in the earlier stages determines how successful the later versions are.
Data miners should assure the data quality they utilize as input for research because bad data quality results in poor outcomes.
Establishing a detailed understanding of the design factors, such as the present business scenario, the project’s main business goal, and the performance objectives.
Identifying the data required to address the problem as well as collecting this from all sorts of sources.
Addressing any errors and bugs, like incomplete or duplicate data, and processing the data in a suitable format to solve the research questions.
Algorithms are used to find patterns from data.
Identifying if or how another model’s output will contribute to the achievement of a business objective.
In order to acquire the optimum outcome, an iterative process is frequently used to identify the best method.
Getting the project’s findings suitable for making decisions in real-time

The techniques and actions listed above are repeated until the best outcomes are achieved. Our engineers and developers have extensive knowledge of the tools, techniques, and approaches used in the processes described above. We guarantee that we will provide the best research advice w.r.t to data mining thesis topics and complete your project on schedule. What are the important data mining tasks?

Data Mining Tasks

Data mining finds application in many ways including description, Analysis, summarization of data, and clarifying the conceptual understanding by data description
And also prediction, classification, dependency analysis, segmentation, and case-based reasoning are some of the important data mining tasks
Regression – numerical data prediction (stock prices, temperatures, and total sales)
Data warehousing – business decision making and large-scale data mining
Classification – accurate prediction of target classes and their categorization
Association rule learning – market-based analytical tools that were involved in establishing variable data set relationship
Machine learning – statistical probability-based decision making method without complicated programming
Data analytics – digital data evaluation for business purposes
Clustering – dataset partitioning into clusters and subclasses for analyzing natural data structure and format
Artificial intelligence – human-based Data analytics for reasoning, solving problems, learning, and planning
Data preparation and cleansing – conversion of raw data into a processed form for identification and removal of errors

You can look at our website for a more in-depth look at all of these operations. We supply you with the needed data, as well as any additional data you may need for your data mining thesis topics . We supply non-plagiarized data mining thesis assistance in any fresh idea of your choice. Let us now discuss the stages in data mining that are to be included in your thesis topics

How to work on a data mining thesis topic?

The following are the important stages or phases in developing data mining thesis topics.

First of all, you need to identify the present demand and address the question
The next step is defining or specifying the problem
Collection of data is the third step
Alternative solutions and designs have to be analyzed in the next step
The proposed methodology has to be designed
The system is then to be implemented

Usually, our experts help in writing codes and implementing them successfully without hassles . By consistently following the above steps you can develop one of the best data mining thesis topics of recent days. Furthermore, technically it is important for you to have a better idea of all the tasks and techniques involved in data mining about which we have discussed below

Data visualization
Neural networks
Statistical modeling
Genetic algorithms and neural networks
Decision trees and induction
Discriminant analysis
Induction techniques
Association rules and data visualization
Bayesian networks
Correlation
Regression analysis
Regression analysis and regression trees

If you are looking forward to selecting the best tool for your data mining project then evaluating its consistency and efficiency stands first. For this, you need to gain enough technical data from real-time executed projects for which you can directly contact us. Since we have delivered an ample number of data mining thesis topics successfully we can help you in finding better solutions to all your research issues. What are the points to be remembered about the data mining strategy?

Furthermore, data mining strategies must be picked before instruments in order to prevent using strategies that do not align with the article’s true purposes.
The typical data mining strategy has always been to evaluate a variety of methodologies in order to select one which best fits the situation.
As previously said, there are some principles that may be used to choose effective strategies for data mining projects.
Since they are easy to handle and comprehend
They could indeed collaborate with definitional and parametric data
Tare unaffected by critical values, they could perhaps function with incomplete information
They could also expose various interrelationships and an absence of linear combinations
They could indeed handle noise in records
They can process huge amounts of data.
Decision trees, on the other hand, have significant drawbacks.
Many rules are frequently necessary for dependent variables or numerous regressions, and tiny changes in the data can result in very different tree architectures.

All such pros and cons of various data mining aspects are discussed on our website. We will provide you with high-quality research assistance and thesis writing assistance . You may see proof of our skill and the unique approach that we generated in the field by looking at the samples of the thesis that we produced on our website. We also offer an internal review to help you feel more confident. Let us now discuss the recent data mining methodologies

Current methods in Data Mining

Prediction of data (time series data mining)
Discriminant and cluster analysis
Logistic regression and segmentation

Our technical specialists and technicians usually give adequate accurate data, a thorough and detailed explanation, and technical notes for all of these processes and algorithms. As a result, you can get all of your questions answered in one spot. Our technical team is also well-versed in current trends, allowing us to provide realistic explanations for all new developments. We will now talk about the latest data mining trends

Latest Trending Data Mining Thesis Topics

Visual data mining and data mining software engineering
Interaction and scalability in data mining
Exploring applications of data mining
Biological and visual data mining
Cloud computing and big data integration
Data security and protecting privacy in data mining
Novel methodologies in complex data mining
Data mining in multiple databases and rationalities
Query language standardization in data mining
Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining

These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis . We design the plan in a chronological order relevant to the study assessment with this in mind. The incorporation of citations is one of the most important aspects of the thesis. We focus not only on authoring but also on citing essential sources in the text. Students frequently struggle to deal with appropriate proposals when commencing their thesis. We have years of experience in providing the greatest study and data mining thesis writing services to the scientific community, which are promptly and widely acknowledged. We will now talk about future research directions of research in various data mining thesis topics

Future Research Directions of Data Mining

The potential of data mining and data science seems promising, as the volume of data continues to grow.
It is expected that the total amount of data in our digital cosmos will have grown from 4.4 zettabytes to 44 zettabytes.
We’ll also generate 1.7 gigabytes of new data for every human being on this planet each second.
Mining algorithms have completely transformed as technology has advanced, and thus have tools for obtaining useful insights from data.
Only corporations like NASA could utilize their powerful computers to examine data once upon a time because the cost of producing and processing data was simply too high.
Organizations are now using cloud-based data warehouses to accomplish any kinds of great activities with machine learning, artificial intelligence, and deep learning.

The Internet of Things as well as wearable electronics, for instance, has transformed devices to be connected into data-generating engines which provide limitless perspectives into people and organizations if firms can gather, store, and analyze the data quickly enough. What are the aspects to be remembered for choosing the best data mining thesis topics?

An excellent thesis topic is a broad concept that has to be developed, verified, or refuted.
Your thesis topic must capture your curiosity, as well as the involvement of both the supervisor and the academicians.
Your thesis topic must be relevant to your studies and should be able to withstand examination.

Our engineers and experts can provide you with any type of research assistance on any of these data mining development tools . We satisfy the criteria of your universities by ensuring several revisions, appropriate formatting and editing of your thesis, comprehensive grammar check, and so on . As a result, you can contact us with confidence for complete assistance with your data mining thesis. What are the important data mining thesis topics?

Trending Data Mining Research Thesis Topics

Research Topics in Data Mining

Handling cost-effective, unbalanced non-static data
Issues related to data mining and their solutions
Network settings in data mining and ensuring privacy, security, and integrity of data
Environmental and biological issues in data mining
Complex data mining and sequential data mining (time series data)
Data mining at higher dimensions
Multi-agent data mining and distributed data mining
High-speed data mining
Development of unified data mining theory

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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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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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UKnowledge > College of Engineering > Mining Engineering > Theses & Dissertations

Theses and Dissertations--Mining Engineering

Theses/dissertations from 2024 2024.

THE METHODOLOGY FOR INTEGRATING ROBOTIC SYSTEMS IN UNDEGROUND MINING MACHINES , Peter Kolapo

DISCRETE ELEMENT MODELING TO PREDICT MUCKPILE PROFILES FROM CAST BLASTING , Russell Lamont

AUTONOMOUS SHUTTLE CAR DOCKING TO A CONTINUOUS MINER USING RGB-DEPTH IMAGERY , Sky Rose

Theses/Dissertations from 2023 2023

ASSESSMENT OF AIR OVERPRESSURE FROM BLASTING USING COMPUTATIONAL FLUID DYNAMICS , Cecilia Estefania Aramayo

RECOVERY OF VALUABLE METALS FROM ELECTRONIC WASTE USING A NOVEL AMMONIA-BASED HYDROMETALLURGICAL PROCESS , Peijia Lin

AN ACID BAKING APPROACH TO ENHANCE RARE EARTH ELEMENT RECOVERY FROM BITUMINOUS COAL SOURCES , Ahmad Nawab

PREDICTION OF DYNAMIC SUBSIDENCE IN THE PROXIMITY OF LONGWALL PANEL BOUNDARIES , JESUS DAVID ROMERO BENITEZ

Prediction of Blast-Induced Ground Vibrations: A Comparison Between Empirical and Artificial-Neural-Network Approaches , Luis F. Velasquez

A LABORATORY AND NUMERICAL INVESTIGATION OF THE STRENGTH OF IRREGULARLY SHAPED PILLARS , Zachary Wedding

Theses/Dissertations from 2022 2022

DEVELOPMENT OF UNIVARIATE AND MULTIVARIATE FORECASTING MODELS FOR METHANE GAS EMISSIONS IN UNDERGROUND COAL MINES , Juan Diaz

PARAMETRIC NUMERICAL ANALYSIS OF INCLINED COAL PILLARS , Robin Flattery

Strain Energy Analysis Related To Strata Failure During Caving Operations , Caroline Gerwig

LAPTOP RECYCLING CASE STUDY: ESTIMATING THE CONTAINED VALUE AND VALUE RECOVERY PROCESS FEASIBILITY OF END-OF-LIFE CONSUMER ELECTRONICS , Zebulon Hart

INVESTIGATION INTO, & ANALYSIS OF TEMPERATURE & STRAIN DATA FOR COAL MINE SEAL MATERIAL DURING CURING , Stephanus Jaco van den Berg

Theses/Dissertations from 2021 2021

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES , Vasileios Androulakis

Investigation of Coal Burst Potential Using Numerical Modeling and Rock Burst Indices , Cristian David Cardenas Triana

Capture of Respirable Dust using Maintenance Free Impingement Screen , Neeraj Kumar Gupta

OXIDATION PRETREATMENT FOR ENHANCED LEACHABILITY OF RARE EARTH ELEMENTS FROM BITUMINOUS COAL SOURCES , Tushar Gupta

AN APPROACH FOR PREDICTING FLOW CHARACTERISTICS AT THE CONTINUOUS MINER FACE , Kayla Henderson

CONCEPTS FOR DEVELOPMENT OF SHUTTLE CAR AUTONOMOUS DOCKING WITH CONTINUOUS MINER USING 3-D DEPTH CAMERA , Sibley Miller

MODELING OF RARE EARTH SOLVENT EXTRACTION PROCESS FOR FLOWSHEET DESIGN AND OPTIMIZATION , Vaibhav Kumar Srivastava

Application of a Novel Ventilation Simplification Algorithm , Caitlin V. Strong

A METHODOLOGY FOR AUTONOMOUS ROOF BOLT INSTALLATION USING INDUSTRIAL ROBOTICS , Anastasia Xenaki

Theses/Dissertations from 2020 2020

NUMERICAL APPROXIMATION OF THE GROUND REACTION AND SUPPORT REACTION CURVES FOR UNDERGROUND LIMESTONE MINES , Jesus Castillo Gomez

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What Is Big Data?

Sherry Tiao | Senior Manager, AI & Analytics, Oracle | March 11, 2024

In This Article

Big Data Defined

The three “vs” of big data, the value—and truth—of big data, the history of big data, big data use cases, big data challenges, how big data works, big data best practices.

What exactly is big data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.”

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

Volume	The amount of data matters. With big data, you’ll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as X (formerly Twitter) data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.
Velocity	Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action.
Variety	Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a . With the rise of big data, data comes in new unstructured data types. Unstructured and semistructured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata.

Two more Vs have emerged over the past few years: value and veracity . Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data—and how much can you rely on it?

Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.

Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

But how did we get here?

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

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Big Data Benefits

Big data makes it possible for you to gain more complete answers because you have more information.
More complete answers mean more confidence in the data—which means a completely different approach to tackling problems.

Big data can help you address a range of business activities, including customer experience and analytics. Here are just a few.

Product development	Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products.
Predictive maintenance	Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime.
Customer experience	The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.
Fraud and compliance	When it comes to security, it’s not just a few rogue hackers—you’re up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.
Machine learning	Machine learning is a hot topic right now. And data—specifically big data—is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible.
Operational efficiency	Operational efficiency may not always make the news, but it’s an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand.
Drive innovation	Big data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities.

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New ways you can use your data
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Benefits and challenges of different use cases

While big data holds a lot of promise, it is not without its challenges.

First, big data is…big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.

But it’s not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that’s relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.

Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.

Discover more big data resources:

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1. Integrate Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.

During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.

2. Manage Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3. Analyze Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

To help you on your big data journey, we’ve put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.

Align big data with specific business goals	More extensive data sets enable you to make new discoveries. To that end, it is important to base new investments in skills, organization, or infrastructure with a strong business-driven context to guarantee ongoing project investments and funding. To determine if you are on the right track, ask how big data supports and enables your top business and IT priorities. Examples include understanding how to filter web logs to understand ecommerce behavior, deriving sentiment from social media and customer support interactions, and understanding statistical correlation methods and their relevance for customer, product, manufacturing, and engineering data.
Ease skills shortage with standards and governance	One of the biggest obstacles to benefiting from your investment in big data is a skills shortage. You can mitigate this risk by ensuring that big data technologies, considerations, and decisions are added to your IT governance program. Standardizing your approach will allow you to manage costs and leverage resources. Organizations implementing big data solutions and strategies should assess their skill requirements early and often and should proactively identify any potential skill gaps. These can be addressed by training/cross-training existing resources, hiring new resources, and leveraging consulting firms.
Optimize knowledge transfer with a center of excellence	Use a center of excellence approach to share knowledge, control oversight, and manage project communications. Whether big data is a new or expanding investment, the soft and hard costs can be shared across the enterprise. Leveraging this approach can help increase big data capabilities and overall information architecture maturity in a more structured and systematic way.
Top payoff is aligning unstructured with structured data	It is certainly valuable to analyze big data on its own. But you can bring even greater business insights by connecting and integrating low density big data with the structured data you are already using today. Whether you are capturing customer, product, equipment, or environmental big data, the goal is to add more relevant data points to your core master and analytical summaries, leading to better conclusions. For example, there is a difference in distinguishing all customer sentiment from that of only your best customers. Which is why many see big data as an integral extension of their existing business intelligence capabilities, data warehousing platform, and information architecture. Keep in mind that the big data analytical processes and models can be both human- and machine-based. Big data analytical capabilities include statistics, spatial analysis, semantics, interactive discovery, and visualization. Using analytical models, you can correlate different types and sources of data to make associations and meaningful discoveries.
Plan your discovery lab for performance	Discovering meaning in your data is not always straightforward. Sometimes we don’t even know what we’re looking for. That’s expected. Management and IT needs to support this “lack of direction” or “lack of clear requirement.” At the same time, it’s important for analysts and data scientists to work closely with the business to understand key business knowledge gaps and requirements. To accommodate the interactive exploration of data and the experimentation of statistical algorithms, you need high-performance work areas. Be sure that sandbox environments have the support they need—and are properly governed.
Align with the cloud operating model	Big data processes and users require access to a broad array of resources for both iterative experimentation and running production jobs. A big data solution includes all data realms including transactions, master data, reference data, and summarized data. Analytical sandboxes should be created on demand. Resource management is critical to ensure control of the entire data flow including pre- and post-processing, integration, in-database summarization, and analytical modeling. A well-planned private and public cloud provisioning and security strategy plays an integral role in supporting these changing requirements.

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Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning .

Companies employ predictive analytics to find patterns in this data to identify risks and opportunities. Predictive analytics is often associated with big data and data science .

Today, companies today are inundated with data from log files to images and video, and all of this data resides in disparate data repositories across an organization. To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. Some of these statistical techniques include logistic and linear regression models, neural networks and decision trees. Some of these modeling techniques use initial predictive learnings to make additional predictive insights.

Read why IBM was named a leader in the IDC MarketScape: Worldwide AI Governance Platforms 2023 report.

Predictive analytics models are designed to assess historical data, discover patterns, observe trends, and use that information to predict future trends. Popular predictive analytics models include classification, clustering, and time series models.

Classification models

Classification models fall under the branch of supervised machine learning models. These models categorize data based on historical data, describing relationships within a given dataset. For example, this model can be used to classify customers or prospects into groups for segmentation purposes. Alternatively, it can also be used to answer questions with binary outputs, such answering yes or no or true and false; popular use cases for this are fraud detection and credit risk evaluation. Types of classification models include logistic regression , decision trees, random forest, neural networks, and Naïve Bayes.

Clustering models

Clustering models fall under unsupervised learning . They group data based on similar attributes. For example, an e-commerce site can use the model to separate customers into similar groups based on common features and develop marketing strategies for each group. Common clustering algorithms include k-means clustering, mean-shift clustering, density-based spatial clustering of applications with noise (DBSCAN), expectation-maximization (EM) clustering using Gaussian Mixture Models (GMM), and hierarchical clustering.

Time series models

Time series models use various data inputs at a specific time frequency, such as daily, weekly, monthly, et cetera. It is common to plot the dependent variable over time to assess the data for seasonality, trends, and cyclical behavior, which may indicate the need for specific transformations and model types. Autoregressive (AR), moving average (MA), ARMA, and ARIMA models are all frequently used time series models. As an example, a call center can use a time series model to forecast how many calls it will receive per hour at different times of day.

Predictive analytics can be deployed in across various industries for different business problems. Below are a few industry use cases to illustrate how predictive analytics can inform decision-making within real-world situations.

Banking: Financial services use machine learning and quantitative tools to make predictions about their prospects and customers. With this information, banks can answer questions like who is likely to default on a loan, which customers pose high or low risks, which customers are the most lucrative to target resources and marketing spend and what spending is fraudulent in nature.
Healthcare: Predictive analytics in health care is used to detect and manage the care of chronically ill patients, as well as to track specific infections such as sepsis. Geisinger Health used predictive analytics to mine health records to learn more about how sepsis is diagnosed and treated. Geisinger created a predictive model based on health records for more than 10,000 patients who had been diagnosed with sepsis in the past. The model yielded impressive results, correctly predicting patients with a high rate of survival.
Human resources (HR): HR teams use predictive analytics and employee survey metrics to match prospective job applicants, reduce employee turnover and increase employee engagement. This combination of quantitative and qualitative data allows businesses to reduce their recruiting costs and increase employee satisfaction, which is particularly useful when labor markets are volatile.
Marketing and sales: While marketing and sales teams are very familiar with business intelligence reports to understand historical sales performance, predictive analytics enables companies to be more proactive in the way that they engage with their clients across the customer lifecycle. For example, churn predictions can enable sales teams to identify dissatisfied clients sooner, enabling them to initiate conversations to promote retention. Marketing teams can leverage predictive data analysis for cross-sell strategies, and this commonly manifests itself through a recommendation engine on a brand’s website.
Supply chain: Businesses commonly use predictive analytics to manage product inventory and set pricing strategies. This type of predictive analysis helps companies meet customer demand without overstocking warehouses. It also enables companies to assess the cost and return on their products over time. If one part of a given product becomes more expensive to import, companies can project the long-term impact on revenue if they do or do not pass on additional costs to their customer base. For a deeper look at a case study, you can read more about how FleetPride used this type of data analytics to inform their decision making on their inventory of parts for excavators and tractor trailers. Past shipping orders enabled them to plan more precisely to set appropriate supply thresholds based on demand.

An organization that knows what to expect based on past patterns has a business advantage in managing inventories, workforce, marketing campaigns, and most other facets of operation.

Security: Every modern organization must be concerned with keeping data secure. A combination of automation and predictive analytics improves security. Specific patterns associated with suspicious and unusual end user behavior can trigger specific security procedures.
Risk reduction: In addition to keeping data secure, most businesses are working to reduce their risk profiles. For example, a company that extends credit can use data analytics to better understand if a customer poses a higher-than-average risk of defaulting. Other companies may use predictive analytics to better understand whether their insurance coverage is adequate.
Operational efficiency : More efficient workflows translate to improved profit margins. For example, understanding when a vehicle in a fleet used for delivery is going to need maintenance before it’s broken down on the side of the road means deliveries are made on time, without the additional costs of having the vehicle towed and bringing in another employee to complete the delivery.
Improved decision making: Running any business involves making calculated decisions. Any expansion or addition to a product line or other form of growth requires balancing the inherent risk with the potential outcome. Predictive analytics can provide insight to inform the decision-making process and offer a competitive advantage.

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Why tracking air pollution is as easy as riding a bike

by Jessica Colarossi, Boston University

Imagine being able to contribute to scientific research just by riding a bike: your bicycle automatically collects valuable air quality data from the different neighborhoods you pedal through, creating a mobile network of air quality monitors. That's the vision a group of students at Boston University are working toward.

For their senior design project, a team of College of Engineering undergraduate students created a compact air quality sensor pack that can be attached to the front of a bicycle from Bluebikes, Boston's public rental network. As air passes through the sensor box, it measures local levels of carbon dioxide, methane, particulate matter, and nitrous oxides, while also recording temperature and humidity. The sensor is equipped with a GPS to pinpoint where data is being collected, as well as an accelerometer, a device that senses movement of the bike so it knows when to switch on and off.

Squeezing all of those gadgets into a 5"x8" box and ensuring the electrical equipment could be jostled around on a bicycle without damage, all while remaining protected from harsh weather and rain, proved to be a challenge.

"All of the components of the project are equally important," says Sofiya Filippova, who started working on the project in fall 2023. "Because if we don't have the electronics working, or don't have the communication, or don't have the physical enclosure in place, everything falls apart." There was also the added task of making sure the sensor box didn't interfere with the Bluebikes operating system or the rider experience.

Filippova, along with her teammates—Lorenzo Barale, Luisa DiLorenzo, Maya Lobel, Leon Long, Benjamin Pedi, and Kai Raina Tung—tackled all of these elements over the past two semesters with Emily Ryan, an associate professor of mechanical engineering. After months of tinkering and wiring, the team landed on a final design and took the sensor box out for a spin, attaching it to the front basket of a Bluebikes bicycle with small zip ties. The students took turns riding the bike, each through different Boston neighborhoods, and at the end of their test rides, reviewed the recorded data in a cloud-based communication system that showed data points mapped block by block throughout the city.

"You can clearly see where the data is mapped, on the scale of one city block, and so that's a huge success in our eyes," Filippova says. With such promising results and potential, the team won the 2024 Janetos Climate Action Prize, an award given to students working on a high-impact project.

Globally, air pollution is getting worse. Cars, roads, fossil fuel infrastructure, wildfire smoke, industrial facilities , and other human activities often make air dirty and unhealthy. But the quality of the air we breathe can vary from town to town, neighborhood to neighborhood. Ryan, an associate director of IGS, helped lead this project as a way to get a more complete picture of air pollution in Boston.

"Does the current data being collected reflect air quality in every neighborhood? Absolutely not," she says. Currently, Boston reports air quality data from five sensors located around the city—in Kenmore Square, Chinatown, Dorchester, Chelsea, and Roxbury. So, having an expansive network of air sensors constantly collecting and mapping data could provide valuable insights about places where there isn't any air monitoring, and could even help utility companies find areas where there are gas pipes leaking methane, or pinpoint urban heat islands that could use more trees and shade.

"We know that local conditions really affect air quality locally," Ryan says. "The weather affects it, but also the trains, the buses, the highways, so there are a lot of stakeholders who could be interested in this data."

Before you start seeing air sensors attached to public rental bikes, there's a lot more work to be done. The team's goal for the summer is to continue consulting with the company, which has been supportive and interested in the idea, and work on validating the data collected by testing it against commercial air sensors that are already used for research purposes around the BU campus. Ryan is also in conversation with BU Facilities Management & Operations to explore the possibility of adding sensor packs to the BU shuttle buses.

Filippova says she thoroughly enjoyed her experience with the team and will continue working with Ryan for the summer before beginning a master's program in mechanical engineering at BU.

"Our team had the best time together," she says. "I think our ability to produce a good product in the end is because we had so much fun doing it."

Provided by Boston University

This story is republished courtesy of Boston University. Read the original story here .

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EU AI Act: first regulation on artificial intelligence

The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.

A man faces a computer generated figure with programming language in the background

As part of its digital strategy , the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits , such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation.

Learn more about what artificial intelligence is and how it is used

What Parliament wants in AI legislation

Parliament's priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes.

Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems.

Learn more about Parliament’s work on AI and its vision for AI’s future

AI Act: different rules for different risk levels

The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

Unacceptable risk

Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:

Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children
Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics
Biometric identification and categorisation of people
Real-time and remote biometric identification systems, such as facial recognition

Some exceptions may be allowed for law enforcement purposes. “Real-time” remote biometric identification systems will be allowed in a limited number of serious cases, while “post” remote biometric identification systems, where identification occurs after a significant delay, will be allowed to prosecute serious crimes and only after court approval.

AI systems that negatively affect safety or fundamental rights will be considered high risk and will be divided into two categories:

1) AI systems that are used in products falling under the EU’s product safety legislation . This includes toys, aviation, cars, medical devices and lifts.

2) AI systems falling into specific areas that will have to be registered in an EU database:

Management and operation of critical infrastructure
Education and vocational training
Employment, worker management and access to self-employment
Access to and enjoyment of essential private services and public services and benefits
Law enforcement
Migration, asylum and border control management
Assistance in legal interpretation and application of the law.

All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle. People will have the right to file complaints about AI systems to designated national authorities.

Transparency requirements

Generative AI, like ChatGPT, will not be classified as high-risk, but will have to comply with transparency requirements and EU copyright law:

Disclosing that the content was generated by AI
Designing the model to prevent it from generating illegal content
Publishing summaries of copyrighted data used for training

High-impact general-purpose AI models that might pose systemic risk, such as the more advanced AI model GPT-4, would have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.

Content that is either generated or modified with the help of AI - images, audio or video files (for example deepfakes) - need to be clearly labelled as AI generated so that users are aware when they come across such content.

Supporting innovation

The law aims to offer start-ups and small and medium-sized enterprises opportunities to develop and train AI models before their release to the general public.

That is why it requires that national authorities provide companies with a testing environment that simulates conditions close to the real world.

The Parliament adopted the Artificial Intelligence Act in March 2024 . It will be fully applicable 24 months after entry into force, but some parts will be applicable sooner:

The ban of AI systems posing unacceptable risks will apply six months after the entry into force
Codes of practice will apply nine months after entry into force
Rules on general-purpose AI systems that need to comply with transparency requirements will apply 12 months after the entry into force

High-risk systems will have more time to comply with the requirements as the obligations concerning them will become applicable 36 months after the entry into force.

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The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm. Download Full-text.
Data Mining
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
Open Theses
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 ...
PDF The application of data mining methods
This thesis first introduces the basic concepts of data mining, such as the definition of data mining, its basic function, common methods and basic process, and two common data mining methods, classification and clustering. Then a data mining application in network is discussed in detail, followed by a brief introduction on data mining ...
Dissertations / Theses on the topic 'Data mining'
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.
MASTER'S THESIS
CHAPTER1. Introduction. This thesis for the degree of Master in Science and Engineering at Lule a University of Technology was made at the company Agrenshuset Prepress IT AB in Ornsk oldsvik. This particular topic about analysing data was selected after a discussion with Agrenshuset about their current needs.
data mining
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.
(PDF) APPLYING DATA MINING TECHNIQUES OVER BIG DATA
Data mining is concerned with knowledge discovery and finding patterns in. datasets through a process of applying the model to the data [13]. The model, the heart of. the data mining proce ss, is ...
Trending Data Mining Thesis Topics
Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining. These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis.
(PDF) Trends in data mining research: A two-decade review using topic
Address: 20, Myasnitskaya Street, Moscow 101000, Russia. Abstract. This work analyzes the intellectual structure of data mining as a scientiﬁc discipline. T o do this, we use. topic analysis ...
Innovative Research Topics on Data Mining (Latest Titles)
Research Topics on Data Mining offer you creative ideas to prime your future brightly in research. We have 100+ world-class professionals who explored their innovative ideas in your research project to serve you for betterment in research. So We have conducted 500+ workshops throughout the world, and a large number of researchers and students ...
Latest Thesis Topics in Data Mining
Extracting beneficial and relevant data is the major aim of choosing any data set. Step 3 - Preparation of data. You need to prepare and process the data after extracting it. Step 4 - Data modeling. Data modeling and remodelling based on the user requirement is the fourth step. Step 5 - Evaluation.
(PDF) Implementation of Data Mining Techniques for ...
Part-II of the thesis is about Implementing Data Mining Techniques in finding the trends of celebrities death causes over the past decade. The database for training is created from the public and ...
PDF Data Mining in social media: An Analysis of Techniques and Applications
Techniques for data mining in social media Data mining techniques are used in social media to identify trends and patterns in the content that users create. These techniques are very useful for analyzing the data that organizations collect from these platforms. This section discussed about some of the most common data mining techniques used in ...
Educational Data Mining Clustering Approach: Case Study of
This study aims to investigate the potential of educational data mining (EDM) in addressing the issue of delayed completion in undergraduate student thesis courses. Delayed completion of these courses is a common issue that affects both students and higher education institutions. This study employed clustering analysis to create clusters of thesis topics. The research model was constructed ...
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.'.
Mining Engineering Graduate Theses and Dissertations
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 ...
Theses and Dissertations--Mining Engineering
the methodology for integrating robotic systems in undeground mining machines, peter kolapo. pdf. discrete element modeling to predict muckpile profiles from cast blasting, russell lamont. pdf. autonomous shuttle car docking to a continuous miner using rgb-depth imagery, sky rose. theses/dissertations from 2023 pdf
PhD Topics in Computer Science Data Mining
PhD Topics in Computer Science Data Mining is your definitive solution for all your research related issues. When it comes to Computer Science Data Mining, we suggest choosing Weka for data mining as it is platform-independent and possesses language portability, i.e., Java. Topics in Data Mining are an attractive field because of their growing ...
Top 10 Kaggle Machine Learning Projects to Become Data Scientist in
Project 1: Digit Classification System. Idea: In this project, you must create a model to classify hand-written digits using the MNIST dataset. This project is a fundamental introduction to image classification and is often considered a starting point for those new to deep learning.
What Is Big Data?
The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three "Vs.". Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't ...
What Is Data Modeling?
Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. The goal of data modeling to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be ...
What is Predictive Analytics?
What is predictive analytics? Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities ...
Why tracking air pollution is as easy as riding a bike
As air passes through the sensor box, it measures local levels of carbon dioxide, methane, particulate matter, and nitrous oxides, while also recording temperature and humidity. The sensor is ...
EU AI Act: first regulation on artificial intelligence
The Parliament adopted the Artificial Intelligence Act in March 2024. It will be fully applicable 24 months after entry into force, but some parts will be applicable sooner: The ban of AI systems posing unacceptable risks will apply six months after the entry into force. Codes of practice will apply nine months after entry into force.

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Graph Neural Networks for Spatial Transcriptomics

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