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Relevant bibliographies by topics / Predictive Data Mining

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Academic literature on the topic 'Predictive Data Mining'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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Journal articles on the topic "Predictive Data Mining"

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Gothane, Suwarna. "Predictive Analysis In Data Mining Using Weighted Associative Classifier." Indian Journal of Applied Research 1, no. 6 (2011): 115–19. http://dx.doi.org/10.15373/2249555x/mar2012/40.

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Rani, Kumari, and Saini Kavita. "Data Mining and Predictive Analytics for Injection Molding: An Analysis." Indian Journal of Science and Technology 16, no. 45 (2023): 4131–40. https://doi.org/10.17485/IJST/v16i45.2567.

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Abstract <strong>Objectives:</strong> The objective of the research is to focus on the quality product of injection molding for the automobile industry. The root cause of the defects in the product needs to be understood in order to improve the product quality. <strong>Method:</strong> The research represents an industry Standard Process for Data Mining (CRISP-DM) framework for molding quality improvement. The Logistic Regression, AI ML algorithm has been used to develop the model. Because Logistic Regression is a classification supervised algorithm and our dependent variable al

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B, Mohamed Nowfal. "Smart Health Prediction Using Data Mining." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1219–25. https://doi.org/10.22214/ijraset.2025.68454.

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This project focuses on developing a smart health prediction system using data mining techniques to enhance early detection and prevention of heart disease. By integrating electronic health records, medical databases, and wearable device data, the system leverages classification, clustering, and predictive modeling to identify key risk factors and estimate disease likelihood. The proposed approach enables healthcare providers to make informed decisions, personalize treatment plans, and implement proactive interventions, ultimately improving patient outcomes and reducing healthcare costs. This

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Duong, Xuan-Lam, and Shu-Yi Liaw. "Comparative Analysis of Data Mining Classification Techniques for Prediction of Problematic Internet Shopping." International Journal of Applied Sciences & Development 3 (June 14, 2024): 82–88. http://dx.doi.org/10.37394/232029.2024.3.7.

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As online shopping has surged, so do disorders on internet purchasing. This study aims to develop and compare predictive models that use data mining methods to predict problematic internet shopping. We used the Artificial Neural Network (ANN), CHAID with bagging, and C5.0 and compared them with traditional logistic regression to construct predictive models on a training cohort of 858 shoppers. Another cohort of 368 buyers was utilized to confirm the accuracy of the predictive model. The accuracy, sensitivity, specificity, and the ROC-AUC were used to assess the predictive performance. The C5.0

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Kumari, Rani, and Kavita Saini. "Data Mining and Predictive Analytics for Injection Molding: An Analysis." Indian Journal Of Science And Technology 16, no. 45 (2023): 4131–40. http://dx.doi.org/10.17485/ijst/v16i45.2567.

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Agrawal, Ankit, Sanchit Misra, Ramanathan Narayanan, Lalith Polepeddi, and Alok Choudhary. "Lung Cancer Survival Prediction using Ensemble Data Mining on Seer Data." Scientific Programming 20, no. 1 (2012): 29–42. http://dx.doi.org/10.1155/2012/920245.

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We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer. Carefully designed preprocessing steps resulted in removal/modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several supervised classification methods were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best

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Tang, Linqiang, and Chen Sian. "Educational Data Mining for Student Performance Prediction." Scalable Computing: Practice and Experience 26, no. 3 (2025): 1551–58. https://doi.org/10.12694/scpe.v26i3.4336.

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The topic of Educational Data Mining (EDM) has gained significant traction in improving the quality of education by identifying patterns and insights through the analysis of data gathered from diverse educational settings. In order to discover important elements that affect educational achievement and to give educators and policymakers with useful insights, this study investigates the use of machine learning techniques in predicting student performance. We use a variety of machine learning methods, such as decision trees, support vector machines, and neural networks, to create predictive model

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Rungta, Sakshi, Vanita Jain, and Akanksha Utreja. "Data Mining Engine using Predictive Analytics." International Journal of Computer Applications 121, no. 5 (2015): 22–26. http://dx.doi.org/10.5120/21537-4545.

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Sreejit Ramakrishnan. "The Importance of Data Mining & Predictive Analysis." international journal of engineering technology and management sciences 7, no. 4 (2023): 593–98. http://dx.doi.org/10.46647/ijetms.2023.v07i04.081.

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Data mining is the process of analyzing enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements. Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. Data mining is a wide-ranging and varied process t

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Егорова, Е. С., and Н. А. Попова. "Data Mining in education: predicting student performance." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 11, no. 2(41) (2023): 3–4. http://dx.doi.org/10.26102/2310-6018/2023.41.2.003.

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Способность прогнозировать академические результаты учащихся имеет ценность для любого учебного заведения, стремящегося улучшить успеваемость и мотивацию студентов. Основываясь на сгенерированных прогнозах, учащимся, выявленным как подверженным риску отчисления или неуспеваемости, может быть оказана поддержка более своевременным образом. В статье рассмотрены различные классификационные модели для прогнозирования успеваемости студентов, используя данные, собранные в университетах г. Пензы. Данные включают сведения о зачислении студентов, а также данные о деятельности, полученные из университетс

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Dissertations / Theses on the topic "Predictive Data Mining"

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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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Books on the topic "Predictive Data Mining"

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Olson, David L., and Desheng Wu. Predictive Data Mining Models. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9664-9.

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Olson, David L., and Desheng Wu. Predictive Data Mining Models. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2543-3.

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von der Hude, Marlis. Predictive Analytics und Data Mining. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-30153-8.

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Finlay, Steven. Predictive Analytics, Data Mining and Big Data. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137379283.

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Nitin, Indurkhya, ed. Predictive data mining: A practical guide. Morgan Kaufmann Publishers, 1998.

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author, Chaouchi Mohamed, and Jung Tommy author, eds. Predictive analytics for dummies. John Wiley & Sons, Inc., 2014.

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Chorianopoulos, Antonios. Effective CRM using predictive analytics. John Wiley & Sons Inc., 2015.

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McCue, Colleen. Data mining and predictive analysis: Intelligence gathering and crime analysis. Butterworth-Heinemann, 2007.

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McCue, Colleen. Data mining and predictive analysis: Intelligence gathering and crime analysis. Elsevier/Butterworth-Heinemann, 2006.

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John, MacGregor. Predictive analysis with SAP: The comprehensive guide. Galileo Press, 2014.

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Book chapters on the topic "Predictive Data Mining"

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Olson, David L., and Desheng Wu. "Data Sets." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_2.

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Olson, David L., and Desheng Wu. "Data Sets." In Predictive Data Mining Models. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2543-3_2.

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Olson, David L., and Desheng Wu. "Knowledge Management." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_1.

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Olson, David L., and Desheng Wu. "Basic Forecasting Tools." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_3.

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Olson, David L., and Desheng Wu. "Multiple Regression." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_4.

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Olson, David L., and Desheng Wu. "Regression Tree Models." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_5.

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Olson, David L., and Desheng Wu. "Autoregressive Models." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_6.

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Olson, David L., and Desheng Wu. "Classification Tools." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_7.

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Olson, David L., and Desheng Wu. "Predictive Models and Big Data." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_8.

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Olson, David L., and Desheng Wu. "Knowledge Management." In Predictive Data Mining Models. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2543-3_1.

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Conference papers on the topic "Predictive Data Mining"

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Mathew, Albin, and Savleen Kaur. "Predictive Maintenance for Manufacturing Using Data Mining Techniques." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692226.

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Indrakumari, R., Vishal Pandey, Ashish Kumar Sinha, and Ankesh Kumar. "Smart Health Diagnosis:Harnessing Data Mining for Predictive Healthcare." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895940.

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Biagiotti, Stephen F., Mark Madden, and Elaine S. Hendren. "Risk Mining: a Predictive Tool to Enhance Pipeline Integrity Assessment." In CORROSION 2002. NACE International, 2002. https://doi.org/10.5006/c2002-02073.

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Abstract Standard methods of evaluating pipeline integrity have stressed index-based and conditional based data assessment processes. Recent works, however, have emphasized the importance of predictive techniques using associations, correlations, sequential patterns and other relationships in evaluating pipeline integrity. Data mining represents a shift from verification-driven data analysis approaches to discovery-driven methods in integrity evaluation. Risk mining involves the analysis of large quantities of data in the process of discovering meaningful new correlations, patterns and trends

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K, Thinakaran, Sindor Sapaev, Valisher Sapayev Odilbekuglu, E. Anitha, Saroo Raj R B, and Rachit Garg. "Improving Disease Diagnosis Through Medical Data Mining and Predictive Analysis: Towards Data-Driven Healthcare." In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11064211.

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Kumar, Pankaj, and Anju Bala. "Intelligent stock data prediction using predictive data mining techniques." In 2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2016. http://dx.doi.org/10.1109/inventive.2016.7830218.

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Menzies, Tim. "Beyond data mining; towards "idea engineering"." In PROMISE '13: 9th International Conference on Predictive Models in Software Engineering. ACM, 2013. http://dx.doi.org/10.1145/2499393.2499401.

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Zinchenko, Tetiana, Esther Galbrun, and Pauli Miettinen. "Mining Predictive Redescriptions with Trees." In 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2015. http://dx.doi.org/10.1109/icdmw.2015.123.

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Ataallah Muhammed, Saja, and Laith R. Flaih. "Predictive Modeling in Healthcare: A Survey of Data Mining Applications." In 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24). Cihan University-Erbil, 2024. http://dx.doi.org/10.24086/cocos2024/paper.1083.

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Healthcare domain is the one that has taken responsibility of human well-being which is aligned with the third sustainable goal of the United Nations (UN). Lately, the use and utilizing of Artificial Intelligence (AI) in healthcare services has been increasing. Data mining and machine learning became very significant in the contemporary era. They are also used in the healthcare domain towards solving a variety of issues from disease diagnosis and classifying to risk assessment and survival prediction. Since healthcare services could be improved with predictive modelling, this paper investigate

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Ferreira, Diana, Hugo Peixoto, Jose Machado, and Antonio Abelha. "Predictive Data Mining in Nutrition Therapy." In 2018 13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO). IEEE, 2018. http://dx.doi.org/10.1109/controlo.2018.8516413.

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Madhulatha, T. Soni, and S. Sameen Fatima. "Mining inflation rate using predictive and descriptive Data Mining techniques." In 2014 International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2014. http://dx.doi.org/10.1109/indiacom.2014.6828133.

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Reports on the topic "Predictive Data Mining"

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Hemmert, K. Scott, and D. Eric Johnson. Toward improved branch prediction through data mining. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/993886.

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Wei, Yin-Loh. Decision Trees for Prediction and Data Mining. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada430178.

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo

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Tiwari, Seemant. Data mining with wavelet analysis are used to create a combined renewable energy prediction model. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2303p7706702.

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