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Rough Sets in Knowledge Discovery 2

Applications, Case Studies and Software Systems

  • Lech Polkowski
  • Andrzej Skowron

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 19)

Table of contents

  1. Front Matter
    Pages I-X
  2. Introducing the Book

    1. Lech Polkowski, Andrzej Skowron
      Pages 1-9
  3. Applications

    1. Front Matter
      Pages 11-11
    2. Salvatore Greco, Benedetto Matarazzo, Roman Słowiński
      Pages 13-36
    3. Krzysztof Krawiec, Roman Słowiński, Daniel Vanderpooten
      Pages 37-54
    4. Sinh Hoa Nguyen, Andrzej Skowron, Piotr Synak
      Pages 55-97
    5. Jaroslaw Stepaniuk
      Pages 109-126
    6. Ning Zhong, Ju-Zhen Dong, Setsuo Ohsuga
      Pages 127-144
  4. Case Studies

    1. Front Matter
      Pages 145-145
    2. Andrzej Czyżewski
      Pages 147-165
    3. Kaname Funakoshi, Tu Bao Ho
      Pages 166-177
    4. Adam Mrózek, Leszek Płonka
      Pages 214-237
    5. Adam Mrózek, Krzysztof Skabek
      Pages 238-271
    6. Krzysztof Słowiński, Jerzy Stefanowski
      Pages 272-294
    7. Hideo Tanaka, Yutaka Maeda
      Pages 295-306
    8. Dirk Van den Poel
      Pages 324-335
  5. Hybrid Approaches

  6. Back Matter
    Pages 489-601

About this book

Introduction

The papers on rough set theory and its applications placed in this volume present a wide spectrum of problems representative to the present. stage of this theory. Researchers from many countries reveal their rec.ent results on various aspects of rough sets. The papers are not confined only to mathematical theory but also include algorithmic aspects, applications and information about software designed for data analysis based on this theory. The volume contains also list of selected publications on rough sets which can be very useful to every one engaged in research or applications in this domain and sometimes perhaps unaware of results of other authors. The book shows that rough set theory is a vivid and vigorous domain with serious results to its credit and bright perspective for future developments. It lays on the crossroads of fuzzy sets, theory of evidence, neural networks, Petri nets and many other branches of AI, logic and mathematics. These diverse connec­ tions seem to be a very fertile feature of rough set theory and have essentially contributed to its wide and rapid expansion. It is worth mentioning that its philosophical roots stretch down from Leibniz, Frege and Russell up to Popper. Therefore many concepts dwelled on in rough set theory are not entirely new, nevertheless the theory can be viewed as an independent discipline on its own rights. Rough set theory has found many interesting real life applications in medicine, banking, industry and others.

Keywords

artificial neural network classification cognition data analysis data mining database expert system genetic algorithms information system knowledge discovery knowledge-based systems learning neural networks set theory software

Editors and affiliations

  • Lech Polkowski
    • 1
    • 2
  • Andrzej Skowron
    • 3
  1. 1.Institute of MathematicsWarsaw University of TechnologyWarsawPoland
  2. 2.Polish-Japanese Institute of Computer TechniquesWarsawPoland
  3. 3.Institute of MathematicsWarsaw UniversityWarsawPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-7908-1883-3
  • Copyright Information Physica-Verlag Heidelberg 1998
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-2459-9
  • Online ISBN 978-3-7908-1883-3
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site
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