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

Open Topics

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

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

Graph Neural Networks for Spatial Transcriptomics

Type:  Master's Thesis

Prerequisites:

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

Description:

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

Contact : Filippo Guerranti , Alessandro Palma

References:

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

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

Generative Models for Drug Discovery

Type:  Mater Thesis / Guided Research

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

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

Contact :  Johanna Sommer , Leon Hetzel

Equivariant Diffusion for Molecule Generation in 3D

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

Structure-based Drug Design with Equivariant Diffusion Models

Efficient Machine Learning: Pruning, Quantization, Distillation, and More - DAML x Pruna AI

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 work in collaboration with the DAML research group and the Pruna AI startup on investigating 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

A Machine Learning Perspective on Corner Cases in Autonomous Driving Perception  

Type: Master's Thesis 

Industrial partner: BMW 

Prerequisites: 

• Strong knowledge in machine learning 
• Knowledge of Semantic Segmentation  
• Good 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 semantic segmentation. While the environment in datasets is controlled in real world application novel class or unknown disturbances can occur. To provide safe autonomous driving these cased must be identified. 

The objective is to explore novel class segmentation and out of distribution approaches for semantic segmentation in the context of corner cases for autonomous driving. 

Contact: Sebastian Schmidt

References: 

• Segmenting Known Objects and Unseen Unknowns without Prior Knowledge 
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos  
• Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family  
• Description of Corner Cases in Automated Driving: Goals and Challenges 

Active Learning for Multi Agent 3D Object Detection 

Type: Master's Thesis  Industrial partner: BMW 

• Knowledge in Object Detection 
• Excellent programming skills 

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.  

• 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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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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Dissertations / Theses on the topic 'Data mining'

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Mrázek, Michal. "Data mining." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400441.

Payyappillil, Hemambika. "Data mining framework." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3807.

Abedjan, Ziawasch. "Improving RDF data with data mining." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7133/.

Liu, Tantan. "Data Mining over Hidden Data Sources." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343313341.

Taylor, Phillip. "Data mining of vehicle telemetry data." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/77645/.

Sherikar, Vishnu Vardhan Reddy. "I2MAPREDUCE: DATA MINING FOR BIG DATA." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/437.

Zhang, Nan. "Privacy-preserving data mining." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1080.

Hulten, Geoffrey. "Mining massive data streams /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6937.

Büchel, Nina. "Faktorenvorselektion im Data Mining /." Berlin : Logos, 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=019006997&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

Shao, Junming. "Synchronization Inspired Data Mining." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-137356.

Wang, Xiaohong. "Data mining with bilattices." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59344.pdf.

Knobbe, Arno J. "Multi-relational data mining /." Amsterdam [u.a.] : IOS Press, 2007. http://www.loc.gov/catdir/toc/fy0709/2006931539.html.

丁嘉慧 and Ka-wai Ting. "Time sequences: data mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226760.

Wan, Chang, and 萬暢. "Mining multi-faceted data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/197527.

García-Osorio, César. "Data mining and visualization." Thesis, University of Exeter, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414266.

Wang, Grant J. (Grant Jenhorn) 1979. "Algorithms for data mining." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38315.

Anwar, Muhammad Naveed. "Data mining of audiology." Thesis, University of Sunderland, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573120.

Santos, José Carlos Almeida. "Mining protein structure data." Master's thesis, FCT - UNL, 2006. http://hdl.handle.net/10362/1130.

Garda-Osorio, Cesar. "Data mining and visualisation." Thesis, University of the West of Scotland, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.742763.

Rawles, Simon Alan. "Object-oriented data mining." Thesis, University of Bristol, 2007. http://hdl.handle.net/1983/c13bda2c-75c9-4bfa-b86b-04ac06ba0278.

Mao, Shihong. "Comparative Microarray Data Mining." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415.

Novák, Petr. "Data mining časových řad." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-72068.

Blunt, Gordon. "Mining credit card data." Thesis, n.p, 2002. http://ethos.bl.uk/.

Niggemann, Oliver. "Visual data mining of graph based data." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962400505.

Li, Liangchun. "Web-based data visualization for data mining." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ35845.pdf.

Al-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.

Zhou, Wubai. "Data Mining Techniques to Understand Textual Data." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3493.

KAVOOSIFAR, MOHAMMAD REZA. "Data Mining and Indexing Big Multimedia Data." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2742526.

Adderly, Darryl M. "Data mining meets e-commerce using data mining to improve customer relationship management /." [Gainesville, Fla.]: University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000500.

Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.

Patel, Akash. "Data Mining of Process Data in Multivariable Systems." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-201087.

Cordeiro, Robson Leonardo Ferreira. "Data mining in large sets of complex data." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22112011-083653/.

XIAO, XIN. "Data Mining Techniques for Complex User-Generated Data." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2644046.

Tong, Suk-man Ivy. "Techniques in data stream mining." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34737376.

Borgelt, Christian. "Data mining with graphical models." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962912107.

Weber, Irene. "Suchraumbeschränkung für relationales Data Mining." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11380447.

Maden, Engin. "Data Mining On Architecture Simulation." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611635/index.pdf.

Drwal, Maciej. "Data mining in distributedcomputer systems." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5709.

Thun, Julia, and Rebin Kadouri. "Automating debugging through data mining." Thesis, KTH, Data- och elektroteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-203244.

Rahman, Sardar Muhammad Monzurur, and mrahman99@yahoo com. "Data Mining Using Neural Networks." RMIT University. Electrical & Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080813.094814.

Guo, Shishan. "Data mining in crystallographic databases." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0012/NQ52854.pdf.

Sun, Wenyi. "Data mining extension for economics." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/5869.

Papadatos, George. "Data mining for lead optimisation." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556989.

Rice, Simon B. "Text data mining in bioinformatics." Thesis, University of Manchester, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488351.

Lin, Zhenmin. "Privacy Preserving Distributed Data Mining." UKnowledge, 2012. http://uknowledge.uky.edu/cs_etds/9.

Tong, Suk-man Ivy, and 湯淑敏. "Techniques in data stream mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34737376.

Luo, Man. "Data mining and classical statistics." Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1304657.

Cai, Zhongming. "Technical aspects of data mining." Thesis, Cardiff University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395784.

Shioda, Romy 1977. "Integer optimization in data mining." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17579.

Lo, Ya-Chin, and 羅雅琴. "Data mining in bioinformatics -- NCBI tools for data mining." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/38227591029165701821.

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PhD Thesis (TeX) on Data Mining Temporal and Indefinite Relations using Numerical Dependencies. University College London

scienceMiner/PhdThesis-DataMining

Folders and files, repository files navigation, data mining temporal and indefinite relations using numerical dependencies, department of computer science, ucl, university of london.

We propose that data mining, the search for useful, non-trivial and previously unknown information within a database, can be successfully performed with Numerical Dependencies (NDs), a generalisation of Functional Dependencies (FDs), to model the data, together with resampling, a computationally intensive statistical sampling process, which allows us to make inferences from temporal and indefinite databases.

We use NDs to model relations containing temporal and indefinite information. We extend the theory of NDs by presenting measures for data mining and generalise the chase procedure, a method for updating a relation to satisfy a constraint set, for NDs. We motivate NDs in real-world applications by introducing a database design tool incorporating evolutionary algorithms.

The consistency problem, that of attempting to find a relation satisfying a set of FDs within an indefinite relation, known to be NP-complete, is studied in the context of using NDs for approximation. We employ resampling, based on taking samples of definite relations from indefinite ones, on incremental sample sizes until an approximate fixpoint is reached, denoting an upper bound on the required sample size. Extensive simulations highlight that resampling to find upper bounds in conjunction with the chase for indefinite relations returns valid approximate solutions.

We also study NDs in temporal sequences of relations for knowledge discovery purposes. Each relation within a sequence is mined for a set of NDs which evolve with updates in data. We introduce a temporal logic for the discovery of rules and properties within these sequences, or subsequences, which includes statistical functions within the temporal operators for time series analysis. We also show that time series data may be analysed using a restricted set of the logic. We apply discovery algorithms to both sequences and resampled sequences, allowing smoothing for trend detection. Investigations, presented herein, show these rules to provide interesting and practicable results.

All simulations were implemented in C++.

The work herein is Copyright 1999-2029 Ethan Collopy and The University of London. No rights are given to reproduce or modify this work without permission.

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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

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

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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

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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

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Bootstrap Hypothesis Tests for Evaluating Subgroup Descriptions in Exceptional Model Mining

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

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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

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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 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

Geomechanical and weathering properties of weak roof shales in coal mines , Hakan Gurgenli

Assessment and evaluation of noise controls on roof bolting equipment and a method for predicting sound pressure levels in underground coal mining , Rudy J. Matetic

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Trending Data Mining Thesis Topics

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Data Mining Topics

There is an extraordinary growth of data being generated and stored in about all arenas of information technology. These data are stored in computers in the form of databases which can then be processed and transferred through computer technology. The large volumes of data make the extraction of useful knowledge out of it and thus increased the demand for efficient tools in order to do the extraction. In order to meet these demands, researchers and scientists have been exploring the ways and methods in data visualization, pattern recognition, neural networks, data processing, etc. This exploration and efforts lead to the emergence of a new research area called Data Mining.

The purpose of researcher pursuing PhD in Data Mining is mainly to manipulate a large amount of data in the database determining the various variables to contribute to the solutions of gap in other researches, their problems, and solutions. To find a good research topic and problem is as important as it is to find a good solution through extensive research. Choosing a topic is the work that a researcher should do or learn to do. As our experts always advise, a novice researcher’s first step should be to first look into the various research topics, researches, and experiments to formulate your own. Following are some PhD topics in Data Mining which can help you take this step a little ahead:

• A stride towards customer review extraction using frequent pattern mining algorithms and soft computing techniques.
• Medical data analysis with the aid of association rule mining and artificial intelligence.
• Intrusion detection and classification with the aid of data mining algorithm and Modified neural network.
• An efficient technique to improve the customer management using Pattern mining algorithm.
• Customer review based e-commerce product rating using frequent pattern mining algorithm and sentiment analysis.
• Opinion mining based detection of fake recommendation in e commerce with the aid of machine learning approach.
• Prediction of user behaviour / emotions based on web usage mining with the aid of artificial intelligence.
• Web usage data classification for web personalization using clustering and machine learning techniques.
• An efficient technique for Disease prediction from medical data using data mining algorithm and ANN classifier.
• An efficient web page recommendation system using optimized frequent pattern mining and Neural classifier.

Coming up with a focused research topic is a necessity for your research because data mining is a broad field which makes topic selection more difficult. The above PhD Topics in Data Mining suggested by our team of research consultants are focused and within the scope. Our team can help you to perform an in-depth research on the interest area to mould and refine the topic from your findings and exploration of research.

To get your PhD topic, write into our team at [email protected] .

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• PhD Thesis on Data Mining

PhD Thesis on Data Mining is a platform to succeed in your thesis in a good way. In view of data mining, let’s first check the meaning of it shortly,  “Data mining is the step to discover the data-centric patterns in a large database.”

Today, it is a peak domain in the ML, DL, and AI!!!

Due to taking part in these concepts, data mining is the  most number-one  domain.  During that time comes to the thesis writing, your study must add with the sound of arguments establish the fact.

When it comes to thesis writing, your research must contribute with reasonable proofs to the research community. In order to safeguard your research in this stage, PhD Thesis on Data Mining simplified thesis writing with our brilliant writers.

In order to this stage, PhD Thesis on Data Mining is an easy step for your thesis writing.  When our thesis writing, you are studying must add with the soundproofs. Now a day, it is a high research field in the ML, DL, SL, DS/DL, and AI!!!

Simple steps for powerful PhD Thesis on Data Mining

• First, share your cravings and requirements with us
• Text mining
• Multimedia mining
• Graph mining etc.
• University rules
• The time limit for your thesis
• Your past research works
• Then, assign with a technical writer
• Another, receive the structure
• After approval, your thesis starts writing
• As you get the first draft of your thesis

Our major goal line of the data mining thesis project is to extract apt knowledge from more complex mixed data sets. For that, we will perform the following practices on raw data sets. It may vary according to your requirements . If you have any queries/revision, then clarify it with our writer.

DISCERN OUR PROCESSING APPROACH IN DATA MINING

• Data preprocessing
• Missing values filling
• Noisy data cleaning
• Normalization
• Aggregation
• Discretization
• Hierarchy generation
• Generalization
• Cube aggregation
• Compression data
• Dimensionality reduction
• Optimal attribute subset selection
• Mutual information
• Optimization algorithms
• Whale optimization
• Spider Monkey Optimization
• Ant lion colony optimization
• Data analysis
• Transitive heuristic algorithm
• Expectation-Maximization
• Fuzzy clustering
• ML (ANN, as well as Decision trees, SVM, and PCA)
• DL techniques (such as DNN, CNN, LSTM, and DBN)
• Least square regression
• Logistic regression
• Lasso regression
• Multivariate regression
• Multiple regression

For the current students, thesis writing in a preferred format is a tough task. Same, we will think wisely while writing your thesis. Probably, a helpful friend will keep at the heart.

Use our PhD thesis on data mining like your friend to save your time and money. In addition, your stress will remove 100% at the PhD journey’s end.  Almost, we will double-check with the proofread research team!!!

Our research areas of data mining

• Sentiment analysis
• Social network analysis
• Frequent item-set mining
• Anomaly detection
• Recommender systems
• Semantic web mining
• Mining using AI
• Bio-medical diagnosis
• Query search systems

Our competence tools for your data mining research

• Rapid Miner
• R-programming

To conclude our PhD thesis on data mining. Stay within our success zone. We will achieve great things in your research…

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

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Develop Proposal Idea

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Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

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Scrutinizing Paper Quality

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Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

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Paper Status Tracking

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Revising Paper Precisely

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Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

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We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

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CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

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PhD Thesis on Data Mining Projects

     PhD Thesis on Data Mining Projects provides you to get well knowledge based innovative idea in your research. We have 100+ well-experienced professionals who ultimately updated themselves in the research area. We benefit nearly 5000+ research scholars and students in a fruitful manner. Data mining is one of the key areas in the research domain as we provide complete research guidance, thesis writing, paper writing, and publication work.

We also have 120 + branches worldwide and give research guidance to our researchers and students. We also have a tie-up with several colleges and universities for giving the best guidance for research. We have the support both online and offline. Our professionals are eagerly waiting to clarify your doubt and also giving research guidance.

Thesis on Data Mining Projects

     PhD Thesis on Data Mining Projects offers you variety of real time applications and international standard research guidance. Initially, we have provided basic information about PhD Thesis on Data Mining, and once you committed with us, we also have provided full guidance for your research area.  Data mining identifies patterns and establishes relationships on a large dataset through the data analysis process .  The extraction of hidden predictive information from a large dataset is also one of the key processes.

We have plenty of alumni support to help to attain the World No.1 institute. Let’s view the list of important processes and tools that are as follows,

Most Essential Methods in Data-Mining

• Classification
• Neighborhoods and Clustering
• Association
• Decision tress
• Sequential patterns
• Long-term (memory) processing
• Combination
• Data implementations and preparation
• Linear regression
• Statistics for prediction
• Data, Counting and probability
• Seeking out incomplete data
• Database analysis
• Efficient handling of complex and relational data
• Text analysis
• Dynamic data dashboard

Let’s see some of important tools and software’s with crisp definition that are as follows,

Data mining tools and software’s.

• It is also one of the machine learning software which contains collection of visualization tools and algorithms for the purpose of creating graphical user interface to easy access functions.

Rapid Miner

• It is also data science software which is used to provide an integrated environment for deep learning, machine learning, text mining and predictive analytics.
• It is also an open source which is used in data mining toolkit for data visualization and machine learning
• It is also a cross platform operating system software which is used for scientific computation, data analysis and data visualization.
• It is also open source software which is used for data analytics, reporting and integration platform.
• KEEL as an open source (License in GPLv3) tool , It provides a simple GUI based on data flow to design experiments also with different datasets and computational intelligence algorithms
• ELKI is an open source (AGPLv3)  data mining   software and also ELKI is a knowledge discovery in databases   (KDD, “data mining”)  software framework
• It is also free and open source software which is used to project specific for data stream mining with concept drift

Apache Mahout

• It is the software which is used to build an environment also for quickly creating scalable performance machine learning applications.
• It is a free andopen source software (License in GNU GPL v2) package. It also provides a graphical use interface for data mining.

R-Programming Tool

• R is an  open source   programming language   and software environment which is used for statistical computing   and graphics that is supported by the R Foundation also for Statistical Computing

Recent Research Topics

• Bigdata with dataflow supercomputing
• Datamining-Apriori, k-Means and also decision tree algorithm applied on flacivirus.
• Data mining-apriori, decision tree, and also support vector machine (SVM) with analysis of relation between aging and also telomere
• Data-mining technique also with usage of prediction of rainfall
• Data mining techniques also with usage of mining VRSEC student learning behavior in moodle sytem
• Food disease prediction also using datamining technique
• Educational datamining information also with usage of visualizing large quantities.
• Datamining applied on automatic medical disease treatment system
• A datamining approach also with emergent sematic patterns in large scale image dataset

               We also believe that the above-mentioned information about PhD Data Mining is enough to better understand data mining. If you are not feeling comfortable with the provided information, you can feel free to contact us. Our online tutor’s service is also available 24 x7 with fully helping minds. You can also contact me through the mail, phone, and team viewer.

Stop struggling with your research……

Get our help, shine your future……., related pages, services we offer.

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Class of 2024: Andreea Sistrunk graduates with a Ph.D., a life lesson, and a motto to live by

• Barbara L. Micale
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Andreea Sistrunk’s motto, “A best solution to everything is up to us to uncover,” evolved on her path to earning a Ph.D. in computer science at Virginia Tech’s Northern Virginia campus.

“In the beginning, I found myself overwhelmed and at times discouraged by how fast technology is advancing,” she said. “As hard as I was trying, I could not get the data I needed for my work.”

Sistrunk’s research for her dissertation is at the intersection of computer science, education policy, and geographical information systems and related to Redistrict , an online software platform built by a team of researchers at the Sanghani Center for Artificial Intelligence and Data Analytics to help school districts with their rezoning efforts. The team included Nathan Self, research associate, and Subhodip Biswas, who earned a Ph.D. in computer science in 2022.

The platform includes geospatial optimization algorithms and uses data analytics to help parents and other stakeholders experiment with redrawing school boundaries toward better understanding of the school rezoning plans and their potential effect on the community; share their comments and concerns about proposed plans; propose changes to boundaries; and even create their own plans.

Specifically, Sistrunk worked on the human-computer interaction aspect of Redistrict.

“As a mother, former teacher, and active community member, I knew that rezoning is a very emotional topic,” Sistrunk said, “but I was not at all prepared for the extremely long and very heated public meetings I attended to gather information for my research. For the most part, people were just shouting at one another and no one was really listening. It was very unproductive.”

Sistrunk’s goal was to use technology to decrease contentious debates and raise awareness among stakeholders about the difficulty of producing a rezoning proposal that offers an optimal solution.

But she had to find out how.

She frequently complained to her advisor, Naren Ramakrishnan , the Thomas L. Phillips Professor of Engineering, director of the Sanghani Center, and artificial intelligence and machine learning lead at the Virginia Tech Innovation Campus , hoping he could provide an answer. Ramakrishnan told her he did not have it, but encouraged her not to give up, assuring her that she “would get there.”

“He was so right,” Sistrunk said. “I came to realize that professors are there to guide us to discover more, to advance the state of the art. But pursuing a Ph.D. is an individual endeavor, not something you undertake just to satisfy a professor’s rigor, to graduate, or to publish research.

You have to persevere and you can only reach your goal when you understand how to advance the state-of-the-art by conquering and overcoming obstacles on your own.”

Working cooperatively with Loudoun County Public Schools through a number of nondisclosure agreements, Sistrunk was finally able to collect and use the data she needed to help community members explore and experiment with the possible consequences of various zoning scenarios. For example, they could access data showing them that a school building space was insufficient for the number of students present in certain areas, and that what they might think is a perfect rezoning plan may be impossible with regard to student distribution and demographics.

With this gained knowledge, participants also had the opportunity to read other users’ comments within the platform, gauge how others were feeling, and reflect on their own perspectives before offering their own viable suggestions and proposals.

The result was a far cry from the numerous meetings she had previously attended, Sistrunk said, and Loudoun County Public Schools was pleased with the number of proposals received and the ideas presented. Ultimately, Redistrict was used by Loudoun County Public Schools in two school re-zonings.

Sistrunk’s work illustrates the problem-based learning concept - emphasized at the Innovation Campus – of embedding within the problem context and solving a relevant problem with impact on the community.

Sistrunk has published her Redistrict research in venues such as the European Conference on Computer-Supported Cooperative Work and the Association for Computing Machinery's Conference on Designing Interactive Systems, Conference on Computer-Supported Cooperative Work and Social Computing, and proceedings on human computer interaction.

“Our hope is that school districts around the country will recognize the value of Redistrict in helping them rezone their schools to provide an equitable education to all communities,” Sistrunk said.

The road to Virginia Tech

Sistrunk’s interest in technology began at an early age in her native Romania. After graduating from a  No. 1-ranked specialized computer science high school, she went on to earn a bachelor’s degree in industrial engineering – with a minor in childhood education - from the Polytechnic Institute Bucharest, Romania. While attending college, she was invited through the Board of European Students of Technology to attend classes at universities including the Warsaw University of Technology in Poland, the University of Gothenburg in Sweden, and Sorbonne University in France. Unable to attend the latter with visa constraints, she was later invited to a course at Paris-Saclay University.

Fast forward to 2014: Sistrunk had lived in Canada where she held positions at Sprint Canada and TD Bank. She was married, a mother to two children, and living in Northern Virginia. She had been teaching mathematics, computer science, and engineering at the middle and high school levels in Arlington and Fairfax counties for five years when she decided to take a break from being a full-time teacher to devote more time to her two young daughters.

The transition included a mom’s night out.  Sistrunk’s choice on how to spend those nights was not typical. She took the opportunity to enroll in a graduate course at Virginia Tech because, she said, “I really missed learning new things.”

From that first course, she eventually applied to the  computer science  program and in fall 2019, she earned a master’s degree with a concentration in data analytics and started her next journey as a Ph.D. student. Her dissertation committee, chaired by Ramakrishnan, included Kurt Luther, Nervo Dias Verdetto, Jim Egenrieder, and Patrick Butler.

While completing her doctoral degree she taught courses in Prince William County public schools, worked under Egenrieder’s guidance as a teaching assistant at the  Thinkabit Lab  at the Virginia Tech Falls Church campus, and currently holds a full-time position as a research scientist in a laboratory outside of the university that focuses on geospatial research. 

“Raising children, working, and pursuing my degree was a testament of grit and endurance, and I am so very grateful to all my professors -  and especially my committee – for a top notch education and giving me strength to go the extra mile in my resolve to find solutions to real world problems,” Sistrunk said. “I will carry this wherever the future takes me.”

Read more about Andreea Sistrunk here:  https://news.vt.edu/college-of-engineering/articles/cs-Andreea-Sistrunk-class-of-2024.html .

Franki Fitterer

202-321-6183

• Class of 2024
• College of Engineering
• Computer Science
• Graduate Education
• Graduate Students
• Innovation Campus
• Sanghani Center for Artificial Intelligence and Data Analytics

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