Editors:
- Provides a unique perspective into the core of data mining and knowledge discovery (DM and KD), combining many theoretical foundations for the behavior and capabilities of various DM and KD methods
- Includes supplementary material: sn.pub/extras
Part of the book series: Massive Computing (MACO, volume 6)
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Table of contents (20 chapters)
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Front Matter
About this book
Editors and Affiliations
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Louisiana State University, Baton Rouge, USA
Evangelos Triantaphyllou
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Consiglio Nazionale delle Ricerche, Rome, Italy
Giovanni Felici
Bibliographic Information
Book Title: Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
Editors: Evangelos Triantaphyllou, Giovanni Felici
Series Title: Massive Computing
DOI: https://doi.org/10.1007/0-387-34296-6
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2006
Hardcover ISBN: 978-0-387-34294-8Published: 21 June 2006
Softcover ISBN: 978-1-4419-4173-2Published: 11 February 2011
eBook ISBN: 978-0-387-34296-2Published: 10 September 2006
Series ISSN: 1569-2698
Series E-ISSN: 2468-8738
Edition Number: 1
Number of Pages: XLVIII, 748
Number of Illustrations: 6 b/w illustrations
Topics: Artificial Intelligence, Information Storage and Retrieval, Operations Research, Management Science, Operations Research/Decision Theory