Overview
- Reports recent research results in rough set research
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 174)
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Table of contents (11 chapters)
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Front Matter
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Theoretical Contributions to Rough Set Theory
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Front Matter
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Rough Hybrid Models to Classification and Attribute Reduction
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Front Matter
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Back Matter
Keywords
About this book
Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.
Editors and Affiliations
Bibliographic Information
Book Title: Rough Set Theory: A True Landmark in Data Analysis
Editors: Ajith Abraham, Rafael Falcón, Rafael Bello
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-540-89921-1
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-540-89920-4Published: 26 February 2009
Softcover ISBN: 978-3-642-10062-8Published: 28 October 2010
eBook ISBN: 978-3-540-89921-1Published: 23 December 2008
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XVI, 324
Topics: Computer-Aided Engineering (CAD, CAE) and Design, Mathematical and Computational Engineering, Artificial Intelligence