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Скачать Data Mining: Knowledge Discovery Methods бесплатно


Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski, Lukasz A. Kurgan, "Data Mining: Knowledge Discovery Methods"
Publisher: Springer | Publication Date: 2007-02-01 | ISBN: 0387333339 | 505 Pages | PDF | 8 MB

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This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects.


Based upon the authors’ previous successful book on data mining and knowledge discovery, this new volume has been extensively expanded, making it an effective instructional tool for advanced-level undergraduate and graduate courses. This book offers:


A suite of exercises at the end of every chapter, designed to enhance the reader’s understanding of the theory and proficiency with the tools presented


Links to all-inclusive instructional presentations for each chapter to ensure easy use in classroom teaching


Extensive appendices covering relevant mathematical material for convenient look-up


Methods for addressing issues related to data privacy and security within the context of data mining, enabling the reader to balance potentially conflicting aims


Summaries and bibliographical notes for each chapter, providing a broader perspective of the concepts and methods described


Researchers, practitioners and students are certain to consider this volume an indispensable resource in successfully accomplishing the goals of their data mining projects.


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