© 1998

Incomplete Information: Rough Set Analysis

  • Ewa Orłowska
  • An up-to-date and highly authoritative account of the current status of the basic theory, extensions and applications of rough sets

  • A must reading for anyone who has a serious interest in information processing and knowledge-based systems


Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 13)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Introduction: What You Always Wanted to Know about Rough Sets

  3. Rough Sets and Decision Rules

    1. Front Matter
      Pages 21-21
    2. Jan G. Bazan, Hung Son Nguyen, Tuan Trung Nguyen, Andrzej Skowron, Jaroslaw Stepaniuk
      Pages 23-57
    3. Chien-Chung Chan, Jerzy W. Grzymala-Busse
      Pages 58-74
    4. Jerzy W. Grzymala-Busse, Paolo Werbrouck
      Pages 75-91
  4. Algebraic Structure of Rough Set Systems

    1. Front Matter
      Pages 93-93
    2. Ivo Düntsch
      Pages 95-108
  5. Dependence Spaces

    1. Front Matter
      Pages 191-191
    2. Miroslav Novotný
      Pages 193-246
    3. Miroslav Novotný
      Pages 247-289
  6. Reasoning about Constraints

    1. Front Matter
      Pages 291-291
    2. Wojciech Buszkowski, Ewa Orlowska
      Pages 293-315
    3. Michael Luxenburger
      Pages 316-343
  7. Indiscernibility-Based Reasoning

    1. Front Matter
      Pages 345-345
    2. Stéphane Demri, Ewa Orlowska
      Pages 347-380
    3. Anna Lissowska-Wójtowicz
      Pages 381-398
    4. Andrzej Skowron, Lech Polkowski
      Pages 399-437
  8. Similarity-Based Reasoning

    1. Front Matter
      Pages 439-439

About this book


The book presents rough set formalisms and methods of modeling and handling incomplete information and motivates their applicability to knowledge representation, knowledge discovery and machine learning. The book focuses on providing representational and inference mechanisms for dealing with two particular aspects of incompleteness, namely indiscernibility and similarity. Those manifestations of particular aspects of incompleteness are inherent in any data structure and any cognitive unit. Knowledge discovered from such an information is uncertain in that it can only be asserted with a tolerance. The methods developed in the book are capable of exposing the limits of that tolerance and of making reliable inferences in the environments where complete information is not available. The framework presented in the book is general and unrestrictive, and yet at the same time captures the relevant features of a great variety of the user's data.


algorithms artificial intelligence calculus classification fuzzy incomplete information information information system knowledge discovery knowledge representation learning logic machine learning modeling set theory

Editors and affiliations

  • Ewa Orłowska
    • 1
  1. 1.Institute of TelecommunicationsWarsawPoland

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