Rough - Granular Computing in Knowledge Discovery and Data Mining

J. Stepaniuk

ISBN 10: 3642089720 / ISBN 13: 9783642089725

Published by Springer, 2010

About this Item

Rough - Granular Computing in Knowledge Discovery and Data Mining | J. Stepaniuk | Taschenbuch | Studies in Computational Intelligence | xiv | Englisch | 2010 | Springer | EAN 9783642089725 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 107117072

Bibliographic Details

• Title: Rough - Granular Computing in Knowledge Discovery and Data Mining
• Publisher: Springer
• Publication Date: 2010
• Language: English
• ISBN 10: 3642089720
• ISBN 13: 9783642089725
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 10D millimeters 235 millimeters width by 155 millimeters height by 10 millimeters depth
• Weight: 271 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book covers methods based on a combination of granular computing, rough sets, and knowledge discovery in data mining (KDD). The discussion of KDD foundations based on the rough set approach and granular computing feature illustrative applications.

From the Back Cover

The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough sets, and knowledge discovery and data mining (KDD). In the book, the KDD foundations based on the rough set approach and granular computing are discussed together with illustrative applications. In searching for relevant patterns or in inducing (constructing) classifiers in KDD, different kinds of granules are modeled. In this modeling process, granules called approximation spaces play a special rule. Approximation spaces are defined by  neighborhoods of objects and measures between sets of objects. In the book, the author underlines the importance of approximation spaces in searching for relevant patterns and other granules on dfferent levels of modeling for compound concept approximations. Calculi on such granules are used for modeling computations on granules in searching for target (sub) optimal granules and their interactions on different levels of hierarchical modeling. The methods based on the combination of granular computing, the rough and fuzzy set approaches allow for an effcient construction of the high quality approximation of compound concepts.