Relevant bibliographies by topics / Predictive Data Mining / Dissertations / Theses
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Dissertations / Theses on the topic 'Predictive Data Mining'

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Published: 3 June 2025
Last updated: 19 July 2025

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1

Li, Bin. "Statistical learning and predictive modeling in data mining." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155058111.

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Chakraborty, Ushashi. "Finding the Most Predictive Data Source in Biological Data." Thesis, North Dakota State University, 2013. https://hdl.handle.net/10365/26567.

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Classification can be used to predict unknown functions of proteins by using known function information. In some cases, multiple sets of data are available for classification where prediction is only part of the problem, and knowing the most reliable source for prediction is also relevant. Our goal is to develop classification techniques to find the most predictive of the multiple data sets that we have in this project. We use existing classification techniques like linear and quadratic classifications and statistical relevance measures like posterior and log p analysis in our proposed algorit
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Li, Wenyan Kusiak Andrew. "Predictive engineering in wind energy a data-mining approach /." [Iowa City, Iowa] : University of Iowa, 2009. http://ir.uiowa.edu/etd/399.

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Kyper, Eric S. "An information criterion for use in predictive data mining /." View online ; access limited to URI, 2006. http://0-wwwlib.umi.com.helin.uri.edu/dissertations/dlnow/3225319.

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Karunaratne, Thashmee M. "Learning predictive models from graph data using pattern mining." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-100713.

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Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. Attempts made to improve their efficiency are often associated with the risk of degrading the performance of the predictive models, creating tradeoffs between the efficiency and effectiveness of the learning. Such a situation is analogous to an optimizatio
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Li, Wenyan. "Predictive engineering in wind energy: a data-mining approach." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/399.

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The large-scale wind energy industry is relatively new and is rapidly expanding. The ability of a wind turbine to extract power from the wind is a function of three main factors: the measured wind speed, the power curve of the turbine, and the ability of the machine to handle wind fluctuations. The key parameter determining wind turbine performance is wind speed and it is normally measured with an anemometer placed at the nacelle of a turbine. The dynamic nature of wind speed, however, is a barrier for applying predictive engineering in wind energy. Traditional approaches based on physical sci
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Liao, ChenHan. "Transaction-filtering data mining and a predictive model for intelligent data management." Thesis, Cranfield University, 2008. http://dspace.lib.cranfield.ac.uk/handle/1826/7027.

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This thesis, first of all, proposes a new data mining paradigm (transaction-filtering association rule mining) addressing a time consumption issue caused by the repeated scans of original transaction databases in conventional associate rule mining algorithms. An in-memory transaction filter is designed to discard those infrequent items in the pruning steps. This filter is a data structure to be updated at the end of each iteration. The results based on an IBM benchmark show that an execution time reduction of 10% - 19% is achieved compared with the base case. Next, a data mining-based predicti
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Gorman, Joe, Glenn Takata, Subhash Patel, and Dan Grecu. "A Constraint-Based Approach to Predictive Maintenance Model Development." International Foundation for Telemetering, 2008. http://hdl.handle.net/10150/606187.

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ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California<br>Predictive maintenance is the combination of inspection and data analysis to perform maintenance when the need is indicated by unit performance. Significant cost savings are possible while preserving a high level of system performance and readiness. Identifying predictors of maintenance conditions requires expert knowledge and the ability to process large data sets. This paper d
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Izad, Shenas Seyed Abdolmotalleb. "Predicting High-cost Patients in General Population Using Data Mining Techniques." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23461.

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In this research, we apply data mining techniques to a nationally-representative expenditure data from the US to predict very high-cost patients in the top 5 cost percentiles, among the general population. Samples are derived from the Medical Expenditure Panel Survey’s Household Component data for 2006-2008 including 98,175 records. After pre-processing, partitioning and balancing the data, the final MEPS dataset with 31,704 records is modeled by Decision Trees (including C5.0 and CHAID), Neural Networks. Multiple predictive models are built and their performances are analyzed using various me
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Burrows, John H. (John Henry). "Predictive and preventive maintenance of mobile mining equipment using vibration data." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=24052.

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This thesis discusses approaches to evaluate the health of mining machinery, based on monitored vibration data. The objective was to develop a means to determine machine health, while operating on-line, without reference to an expert. This approach is based on processing acquired vibration data with artificial neural networks (ANN's). A case study, based on data obtained from the monitoring of locomotives at the Iron Ore Company (IOCC). Real time data patterns, profiles and trends, obtained by processing vibration signals from various points on locomotives, were used to test the developed tech
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Abar, Orhan. "Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/85.

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Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with inte
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Monteiro, António S. "Multiple additive regression trees : a methodology for predictive data mining for fraud detection /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FMonteiro.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2002.<br>Thesis advisor(s): Lyn R. Whitaker, Samuel E. Buttrey. Includes bibliographical references (p. 89-91). Also available online.
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MOUNIKA, REDDY CHANDIRI. "Customer Churn Predictive Heuristics from Operator and Users' Perspective." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13452.

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Telecommunication organizations are confronting in expanding client administration weight as they launch various user-desired services. Conveying poor client encounters puts client connections and incomes at danger. One of the metrics used by telecommunications companies to determine their relationship with customers is “Churn”. After substantial research in the field of churn prediction over many years, Big Data analytics with Data Mining techniques was found to be an efficient way for identifying churn. These techniques are usually applied to predict customer churn by building models, patter
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Tekieh, Mohammad Hossein. "Analysis of Healthcare Coverage Using Data Mining Techniques." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20547.

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This study explores healthcare coverage disparity using a quantitative analysis on a large dataset from the United States. One of the objectives is to build supervised models including decision tree and neural network to study the efficient factors in healthcare coverage. We also discover groups of people with health coverage problems and inconsistencies by employing unsupervised modeling including K-Means clustering algorithm. Our modeling is based on the dataset retrieved from Medical Expenditure Panel Survey with 98,175 records in the original dataset. After pre-processing the data, includi
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Siddiqui, Muazzam. "DATA MINING METHODS FOR MALWARE DETECTION." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2783.

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This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean
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Espinoza, Sofia Elizabeth. "Data mining methods applied to healthcare problems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44903.

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Growing adoption of health information technologies is allowing healthcare providers to capture and store enormous amounts of patient data. In order to effectively use this data to improve healthcare outcomes and processes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various data mining and statistical methods that could be applied to the type of datasets that are found in healthcare research. We discuss the process of identification of appropriate measures and statistical tools, the ana
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Sammouri, Wissam. "Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.

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De nos jours, afin de répondre aux exigences économiques et sociales, les systèmes de transport ferroviaire ont la nécessité d'être exploités avec un haut niveau de sécurité et de fiabilité. On constate notamment un besoin croissant en termes d'outils de surveillance et d'aide à la maintenance de manière à anticiper les défaillances des composants du matériel roulant ferroviaire. Pour mettre au point de tels outils, les trains commerciaux sont équipés de capteurs intelligents envoyant des informations en temps réel sur l'état de divers sous-systèmes. Ces informations se présentent sous la form
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Dissanayake, Manjula. "Evolutionary algorithms and weighting strategies for feature selection in predictive data mining." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/2941.

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The improvements in Deoxyribonucleic Acid (DNA) microarray technology mean that thousands of genes can be profiled simultaneously in a quick and efficient manner. DNA microarrays are increasingly being used for prediction and early diagnosis in cancer treatment. Feature selection and classification play a pivotal role in this process. The correct identification of an informative subset of genes may directly lead to putative drug targets. These genes can also be used as an early diagnosis or predictive tool. However, the large number of features (many thousands) present in a typical dataset pre
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Castro, Porras Alexandra Pollette, and Nunahuanca Juan Pedro Hernández. "Implementación de un modelo predictivo basado en data mining y soportado por sap predictive analytics en retails." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2016. http://hdl.handle.net/10757/620850.

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El proyecto tiene como objetivo implementar un modelo predictivo en empresas retail en base a data mining utilizando la herramienta SAP Predictive Analytics, enfocándonos principalmente en un proceso del área de Planeamiento Comercial, este modelo ayuda a disminuir pérdidas monetarias en la empresa retail prediciendo las ventas. Para el desarrollo del proyecto se realiza una investigación sobre la evolución de SAP Predictive Analytics, información relacionada a la implementación y configuración de la herramienta y casos de éxitos resaltantes de su implementación alrededor del mundo. Después de
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Villalon, Rachelle B. (Rachelle Bentajado). "Data mining, inference, and predictive analytics for the built environment with images, text, and WiFi data." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/115448.

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Thesis: Ph. D. in Architecture Design and Computation, Massachusetts Institute of Technology, Department of Architecture, June 2017.<br>Cataloged from PDF version of thesis. "February 2017."<br>Includes bibliographical references (pages 190-194).<br>What can campus WiFi data tell us about life at MIT? What can thousands of images tell us about the way people see and occupy buildings in real-time? What can we learn about the buildings that millions of people snap pictures of and text about over time? Crowdsourcing has triggered a dramatic shift in the traditional forms of producing content. The
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Monteiro, Antonio Jorge Ferreira da Silva. "Multiple additive regression trees: a methodology for predictive data mining for fraud detection." Thesis, Monterey, California. Naval Postgraduate School, 2002. http://hdl.handle.net/10945/4812.

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Approved for public release, distribution is unlimited<br>The Defense Finance Accounting Service DFAS-Operation Mongoose (Internal Review - Seaside) is using new and innovative techniques for fraud detection. Their primary techniques for fraud detection are the data mining tools of classification trees and neural networks as well as methods for pooling the results of multiple model fits. In this thesis a new data mining methodology, Multiple Additive Regression Trees (MART) is applied to the problem of detecting potential fraudulent and suspect transactions (those with conditions needing impro
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Malhotra, Kunal. "A treatment recommendation tool based on temporal data mining and an automated dynamic database to record evolving data." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53612.

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The thesis examines sequential mining approaches in the context of treatment recommendation for Gliblastoma (GBM) patients. GBM is the most lethal and biologically the most aggressive forms of brain tumor with median survival of approximately 1 year. A significant challenge in treating such rare forms of cancer is to make the best decision about optimal treatment plans for patients after standard of care. We tailor the existing sequential mining approaches by adding constraints to mine significant treatment options for cancer patients. The goal of the work is to analyze which treatment patter
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Aronsson, Henrik. "Modeling strategies using predictive analytics : Forecasting future sales and churn management." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167130.

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This project was carried out for a company named Attollo, a consulting firm specialized in Business Intelligence and Corporate Performance Management. The project aims to explore a new area for Attollo, predictive analytics, which is then applied to Klarna, a client of Attollo. Attollo has a partnership with IBM, which sells services for predictive analytics. The tool that this project is carried out with, is a software from IBM: SPSS Modeler. Five different examples are given of what and how the predictive work that was carried out at Klarna consisted of. From these examples, the different pr
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Daglar, Toprak Seda. "A New Hybrid Multi-relational Data Mining Technique." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606150/index.pdf.

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Multi-relational learning has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. As patterns involve multiple relations, the search space of possible hypotheses becomes intractably complex. Many relational knowledge discovery systems have been developed employing various search strategies, search heuristics and pattern language limitations in order to cope with the complexity of hypothesis space. In this work, we propose a relational concept learning technique, which adopts concept descriptio
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Kadambi, Rupasri. "Analysis of data mining techniques for customer segmentation and predictive modeling a case study /." Diss., Online access via UMI:, 2005.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2005.<br>Includes bibliographical references.
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Ashok, Ashish Kumar. "Predictive data mining in a collaborative editing system: the Wikipedia articles for deletion process." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/12026.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>In this thesis, I examine the Articles for Deletion (AfD) system in /Wikipedia/, a large-scale collaborative editing project. Articles in Wikipedia can be nominated for deletion by registered users, who are expected to cite criteria for deletion from the Wikipedia deletion. For example, an article can be nominated for deletion if there are any copyright violations, vandalism, advertising or other spam without relevant content, advertising or other spam without relevant content. Articles whose subject ma
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König, Rikard. "Enhancing genetic programming for predictive modeling." Doctoral thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3689.

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<p>Avhandling för teknologie doktorsexamen i datavetenskap, som kommer att försvaras offentligt tisdagen den 11 mars 2014 kl. 13.15, M404, Högskolan i Borås. Opponent: docent Niklas Lavesson, Blekinge Tekniska Högskola, Karlskrona.</p>
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Hlongwane, Rivalani Willie. "Selecting the best model for predicting a term deposit product take-up in banking." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29789.

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In this study, we use data mining techniques to build predictive models on data collected by a Portuguese bank through a term savings product campaign conducted between May 2008 and November 2010. This data is imbalanced, given an observed take-up rate of 11.27%. Ling et al. (1998) indicated that predictive models built on imbalanced data tend to yield low sensitivity and high specificity, an indication of low true positive and high true negative rates. Our study confirms this finding. We, therefore, use three sampling techniques, namely, under-sampling, oversampling and Synthetic Minori
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Hagward, Anders. "Using Git Commit History for Change Prediction : An empirical study on the predictive potential of file-level logical coupling". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172998.

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In recent years, a new generation of distributed version control systems have taken the place of the aging centralized ones, with Git arguably being the most popular distributed system today. We investigate the potential of using Git commit history to predict files that are often changed together. Specifically, we look at the rename tracking heuristic found in Git, and the impact it has on prediction performance. By applying a data mining algorithm to five popular GitHub repositories we extract logical coupling – inter-file dependencies not necessarily detectable by static analysis – on which
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Du, Toit Jan Valentine. "Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit." Thesis, North-West University, 2006. http://hdl.handle.net/10394/128.

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In this thesis Generalized Additive Neural Networks (GANNs) are studied in the context of predictive Data Mining. A GANN is a novel neural network implementation of a Generalized Additive Model. Originally GANNs were constructed interactively by considering partial residual plots. This methodology involves subjective human judgment, is time consuming, and can result in suboptimal results. The newly developed automated construction algorithm solves these difficulties by performing model selection based on an objective model selection criterion. Partial residual plots are only utilized after the
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Abounia, Omran Behzad. "Application of Data Mining and Big Data Analytics in the Construction Industry." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934.

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Zhou, Mu. "Knowledge Discovery and Predictive Modeling from Brain Tumor MRIs." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5809.

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Quantitative cancer imaging is an emerging field that develops computational techniques to acquire a deep understanding of cancer characteristics for cancer diagnosis and clinical decision making. The recent emergence of growing clinical imaging data provides a wealth of opportunity to systematically explore quantitative information to advance cancer diagnosis. Crucial questions arise as to how we can develop specific computational models that are capable of mining meaningful knowledge from a vast quantity of imaging data and how to transform such findings into improved personalized health car
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Qiu, Xin Ying. "On building predictive models with company annual reports." Diss., University of Iowa, 2007. http://ir.uiowa.edu/etd/167.

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ATTANASIO, ANTONIO. "Mining Heterogeneous Urban Data at Multiple Granularity Layers." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2709888.

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The recent development of urban areas and of the new advanced services supported by digital technologies has generated big challenges for people and city administrators, like air pollution, high energy consumption, traffic congestion, management of public events. Moreover, understanding the perception of citizens about the provided services and other relevant topics can help devising targeted actions in the management. With the large diffusion of sensing technologies and user devices, the capability to generate data of public interest within the urban area has rapidly grown. For instance, diff
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Yang, Shuang-Hong. "Predictive models for online human activities." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43689.

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The availability and scale of user generated data in online systems raises tremendous challenges and opportunities to analytic study of human activities. Effective modeling of online human activities is not only fundamental to the understanding of human behavior, but also important to the online industry. This thesis focuses on developing models and algorithms to predict human activities in online systems and to improve the algorithmic design of personalized/socialized systems (e.g., recommendation, advertising, Web search systems). We are particularly interested in three types of online user
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Ge, Esther. "The query based learning system for lifetime prediction of metallic components." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/18345/4/Esther_Ting_Ge_Thesis.pdf.

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This research project was a step forward in developing an efficient data mining method for estimating the service life of metallic components in Queensland school buildings. The developed method links together the different data sources of service life information and builds the model for a real situation when the users have information on limited inputs only. A practical lifetime prediction system was developed for the industry partners of this project including Queensland Department of Public Works and Queensland Department of Main Roads. The system provides high accuracy in practice where n
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Ge, Esther. "The query based learning system for lifetime prediction of metallic components." Queensland University of Technology, 2008. http://eprints.qut.edu.au/18345/.

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This research project was a step forward in developing an efficient data mining method for estimating the service life of metallic components in Queensland school buildings. The developed method links together the different data sources of service life information and builds the model for a real situation when the users have information on limited inputs only. A practical lifetime prediction system was developed for the industry partners of this project including Queensland Department of Public Works and Queensland Department of Main Roads. The system provides high accuracy in practice where n
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Al, Nasseri Alya Ali Mansoor. "The predictive power of stock micro-blogging sentiment in forecasting stock market behaviour." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13575.

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Online stock forums have become a vital investing platform on which to publish relevant and valuable user-generated content (UGC) data such as investment recommendations and other stock-related information that allow investors to view the opinions of a large number of users and share-trading ideas. This thesis applies methods from computational linguistics and text-mining techniques to analyse and extract, on a daily basis, sentiments from stock-related micro-blogging messages called “StockTwits”. The primary aim of this research is to provide an understanding of the predictive ability of stoc
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Kane-Sellers, Marjorie Laura. "Predictive models of employee voluntary turnover in a North American professional sales force using data-mining analysis." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1486.

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De, Colle Mattia. "Topological Data Analysis to improve the predictive model of an Electric Arc Furnace." Thesis, KTH, Materialvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-201744.

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Data mining, and in particular topological data analysis (TDA), had proven to be successful inabstracting insights from big arrays of data. This thesis utilizes the TDA software AyasdiTM inorder to improve the accuracy of the energy model of an Electric Arc Furnace (EAF), pinpointingthe causes of a wrong calculation of the steel temperature. Almost 50% of the charges analyzedpresented an underestimation of temperature, while under 30% an overestimation.First a dataset was created by filtering the data obtained by the company. After an initialscreening, around 700 charges built the dataset, eac
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Shew, Cameron Hunter. "TRANSFERABILITY AND ROBUSTNESS OF PREDICTIVE MODELS TO PROACTIVELY ASSESS REAL-TIME FREEWAY CRASH RISK." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/863.

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This thesis describes the development and evaluation of real-time crash risk assessment models for four freeway corridors, US-101 NB (northbound) and SB (southbound) as well as I-880 NB and SB. Crash data for these freeway segments for the 16-month period from January 2010 through April 2011 are used to link historical crash occurrences with real-time traffic patterns observed through loop detector data. The analysis techniques adopted for this study are logistic regression and classification trees, which are one of the most common data mining tools. The crash risk assessment models are develo
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Hosseini, Rahilsadat. "Wastewater's total influent estimation and performance modeling: a data driven approach." Thesis, University of Iowa, 2011. https://ir.uiowa.edu/etd/2716.

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Wastewater treatment plants (WWTP) involve several complex physical, biological and chemical processes. Often these processes exhibit non-linear behavior that is difficult to describe by classical mathematical models. Safer operation and control of a WWTP can be achieved by developing a modeling tool for predicting the plant performance. In the last decade, many studies were realized in wastewater treatment based on intelligent methods which are related to modeling WWTP. These studies are about predictions of WWTP parameters, process control of WWTP, estimating WWTP output parameters character
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Kabir, Md Faisal. "Extracting Useful Information and Building Predictive Models from Medical and Health-Care Data Using Machine Learning Techniques." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31924.

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In healthcare, a large number of medical data has emerged. To effectively use these data to improve healthcare outcomes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various machine learning (ML) and data mining (DM) methods that could be applied to the type of data sets that are available in the healthcare area. The first part of the dissertation investigates DM methods on healthcare or medical data to find significant information in the form of rules. Class association rule mining, a v
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Pardos, Zachary Alexander. "Predictive Models of Student Learning." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-dissertations/185.

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In this dissertation, several approaches I have taken to build upon the student learning model are described. There are two focuses of this dissertation. The first focus is on improving the accuracy with which future student knowledge and performance can be predicted by individualizing the model to each student. The second focus is to predict how different educational content and tutorial strategies will influence student learning. The two focuses are complimentary but are approached from slightly different directions. I have found that Bayesian Networks, based on belief propagation, are stron
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Patti, Alexandra C. "Technology and Big Data Meet the Risk of Terrorism in an Era of Predictive Policing and Blanket Surveillance." ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/2014.

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Surveillance studies suffer from a near-total lack of empirical data, partially due to the highly secretive nature of surveillance programs. However, documents leaked by Edward Snowden in June of 2013 provided unprecedented proof of top-secret American data mining initiatives that covertly monitor electronic communications, collect, and store previously unfathomable quantities of data. These documents presented an ideal opportunity for testing theory against data to better understand contemporary surveillance. This qualitative content analysis compared themes of technology, privacy, national s
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Wärn, Caroline. "Deviating time-to-onset in predictive models : detecting new adverse effects from medicines." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-257100.

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Identifying previously unknown adverse drug reactions becomes more important as the number of drugs and the extent of their use increases. The aim of this Master’s thesis project was to evaluate the performance of a novel approach for highlighting potential adverse drug reactions, also known as signal detection. The approach was based on deviating time-to-onset patterns and was implemented as a two-sample Kolmogorov-Smirnov test for non-vaccine data in the safety report database, VigiBase. The method was outperformed by both disproportionality analysis and the multivariate predictive model vig
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Kratsch, Wolfgang [Verfasser], and Maximilian [Akademischer Betreuer] Röglinger. "Data-driven Management of Interconnected Business Processes : Contributions to Predictive and Prescriptive Process Mining / Wolfgang Kratsch ; Betreuer: Maximilian Röglinger." Bayreuth : Universität Bayreuth, 2021. http://d-nb.info/122950544X/34.

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Sowan, Bilal I. "Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stag
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Whitlock, Joshua Lee. "Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3356.

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The purpose of this study was to discover factors about first-time freshmen that began at one of the six 4-year universities in the former Tennessee Board of Regents (TBR) system, transferred to any other institution after their first year, and graduated with a degree or certificate. These factors would be used with predictive models to identify these students prior to their initial departure. Thirty-four variables about students and the institutions that they attended and graduated from were used to perform principal component analysis to examine the factors involved in their decisions. A sub
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Medina, Erik Cevallos, Claudio Barahona Chunga, Jimmy Armas-Aguirre, and Elizabeth E. Grandon. "Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees." IEEE Computer Society, 2020. http://hdl.handle.net/10757/656775.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>This research proposes a prediction model that might help reducing the dropout rate of university students in Peru. For this, a three-phase predictive analysis model was designed which was combined with the stages proposed by the IBM SPSS Modeler methodology. Bayesian network techniques was compared with decision trees for their level of accuracy over other algorithms in an Educational Data Mining (EDM) scenario. Data were collected from 500 un
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