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Partial Covers, Reducts and Decision Rules in Rough Sets

Theory and Applications

  • Authors
  • Mikhail Ju. Moshkov
  • Marcin Piliszczuk
  • Beata Zielosko

Part of the Studies in Computational Intelligence book series (SCI, volume 145)

Table of contents

  1. Front Matter
  2. Mikhail Ju. Moshkov, Marcin Piliszczuk, Beata Zielosko
    Pages 1-6
  3. Mikhail Ju. Moshkov, Marcin Piliszczuk, Beata Zielosko
    Pages 7-49
  4. Mikhail Ju. Moshkov, Marcin Piliszczuk, Beata Zielosko
    Pages 51-96
  5. Mikhail Ju. Moshkov, Marcin Piliszczuk, Beata Zielosko
    Pages 117-133
  6. Mikhail Ju. Moshkov, Marcin Piliszczuk, Beata Zielosko
    Pages 135-142
  7. Back Matter

About this book

Introduction

This monograph is devoted to theoretical and experimental study of partial reducts and partial decision rules on the basis of the study of partial covers. The use of partial (approximate) reducts and decision rules instead of exact ones allows us to obtain more compact description of knowledge contained in decision tables, and to design more precise classifiers. Algorithms for construction of partial reducts and partial decision rules, bounds on minimal complexity of partial reducts and decision rules, and algorithms for construction of the set of all partial reducts and the set of all irreducible partial decision rules are considered. The book includes a discussion on the results of numerous experiments with randomly generated and real-life decision tables. These results show that partial reducts and decision rules can be used in data mining and knowledge discovery both for knowledge representation and for prediction.

The results obtained in the monograph can be useful for researchers in such areas as machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, test theory and LAD (Logical Analysis of Data). The monograph can be used under the creation of courses for graduate students and for Ph.D. studies.

Keywords

Analysis algorithm algorithms complexity construction data mining knowledge knowledge discovery knowledge representation learning machine learning

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-69029-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-69027-6
  • Online ISBN 978-3-540-69029-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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