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Data mining fundamentals : a tutorial based primer / Richard J. Roiger.

By: Roiger, Richard J [author.]Material type: TextTextSeries: Chapman & Hall/CRC data mining and knowledge discovery seriesPublisher: Boca Raton : CRC Press, Taylor & Francis Group, [2017]Copyright date: ©2017Edition: Second editionDescription: xli, 487 pages : illustrations ; 26 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781498763974; 1498763979Subject(s): Data miningDDC classification: 006.312
Contents:
Section I. Data Mining Fundamentals -- Data mining: a first view -- Data mining: a closer look -- Basic data mining techniques -- Section II. Tools for knowledge discovery. Weka--an environment for knowledge discovery -- Knowledge discovery with RapidMiner -- The knowledge discovery process -- Formal evaluation techniques -- Section III. Building neural networks -- Neural networks -- Building Neural networks with Weka -- Building neural networks with RapidMiner -- Section IV. Advanced data mining techniques -- Supervised statistical techniques -- Unsupervised clustering techniques -- Specialized techniques -- The data warehouse -- Appendix A. Software and datasets for data mining -- Appendix B. Statistics for performance evaluation.
Summary: "Provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well known software tools."-- Cover page 4.
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Includes bibliographical references (pages 461-464) and index.

Section I. Data Mining Fundamentals -- Data mining: a first view -- Data mining: a closer look -- Basic data mining techniques -- Section II. Tools for knowledge discovery. Weka--an environment for knowledge discovery -- Knowledge discovery with RapidMiner -- The knowledge discovery process -- Formal evaluation techniques -- Section III. Building neural networks -- Neural networks -- Building Neural networks with Weka -- Building neural networks with RapidMiner -- Section IV. Advanced data mining techniques -- Supervised statistical techniques -- Unsupervised clustering techniques -- Specialized techniques -- The data warehouse -- Appendix A. Software and datasets for data mining -- Appendix B. Statistics for performance evaluation.

"Provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well known software tools."-- Cover page 4.

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