Rough – Granular Computing in Knowledge Discovery and Data Mining

  • Jarosław Stepaniuk

Part of the Studies in Computational Intelligence book series (SCI, volume 152)

Table of contents

  1. Front Matter
  2. Introduction

    1. Jarosław Stepaniuk
      Pages 1-9
  3. Part I: Rough Set Methodology

    1. Front Matter
      Pages 11-11
    2. Jarosław Stepaniuk
      Pages 13-41
    3. Jarosław Stepaniuk
      Pages 43-56
  4. Part II: Classification and Clustering

    1. Front Matter
      Pages 57-57
    2. Jarosław Stepaniuk
      Pages 59-66
    3. Jarosław Stepaniuk
      Pages 67-77
    4. Jarosław Stepaniuk
      Pages 79-96
  5. Part III: Complex Data and Complex Concepts

    1. Front Matter
      Pages 97-97
    2. Jarosław Stepaniuk
      Pages 99-110
    3. Jarosław Stepaniuk
      Pages 111-131
  6. Part IV: Conclusions, Bibliography and Further Readings

    1. Front Matter
      Pages 133-133
    2. Jarosław Stepaniuk
      Pages 135-136
  7. Back Matter

About this book


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.


Computational Intelligence Fuzzy Knowledge Discovery Rough - Granular Computing classification construction data mining fuzzy set knowledge modeling

Authors and affiliations

  • Jarosław Stepaniuk
    • 1
  1. 1.Bialystok University of TechnologyBialystokPoland

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-70800-1
  • Online ISBN 978-3-540-70801-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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