Editors:
- Most complete, extensive and modern handbook available today in the field of data mining, the core of the knowledge discovery process
- Algorithmic descriptions are detailed so the reader can understand exactly how they work, and thus implement, modify and intelligently use them
- Includes detailed tutorials, and each topic is supplemented with references for further study
- Includes supplementary material: sn.pub/extras
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Table of contents (68 chapters)
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Front Matter
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Introduction to Knowledge Discovery in Databases
About this book
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.
This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.
Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Editors and Affiliations
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Dept. of Industrial Engineering, Tel-Aviv University, Ramat-Aviv, Israel
Oded Maimon, Lior Rokach
Bibliographic Information
Book Title: Data Mining and Knowledge Discovery Handbook
Editors: Oded Maimon, Lior Rokach
DOI: https://doi.org/10.1007/b107408
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2005
eBook ISBN: 978-0-387-25465-4Published: 28 May 2006
Edition Number: 1
Number of Pages: XXXVI, 1383
Number of Illustrations: 400 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Database Management, Information Storage and Retrieval, Computer Communication Networks, Multimedia Information Systems, Data Storage Representation