Rough Sets and Data Mining

Analysis of Imprecise Data

  • T. Y. Lin
  • N. Cercone

Table of contents

  1. Front Matter
    Pages i-xi
  2. Expositions

    1. Front Matter
      Pages 1-1
    2. Zdzisław Pawlak
      Pages 3-7
    3. Jitender S. Deogun, Vijay V. Raghavan, Amartya Sarkar, Hayri Sever
      Pages 9-45
    4. Y. Y. Yao, S. K. M. Wong, T. Y. Lin
      Pages 47-75
    5. Toshinori Munakata
      Pages 77-88
  3. Applications

    1. Front Matter
      Pages 89-89
    2. Jerzy W. Grzymala-Busse, Sally Yeates Sedelow, Walter A. Sedelow Jr.
      Pages 91-107
    3. Xiaohua Hu, Nick Cercone, Wojciech Ziarko
      Pages 109-121
    4. Zdzisław Pawlak
      Pages 139-147
    5. Ray R. Hashemi, Bruce A. Pearce, Ramin B. Arani, Willam G. Hinson, Merle G. Paule
      Pages 149-175
  4. Related Areas

    1. Front Matter
      Pages 197-197
    2. Nick J. Cercone, Howard J. Hamilton, Xiaohua Hu, Ning Shan
      Pages 199-227
    3. Andrzej Skowron, Lech Polkowski
      Pages 259-299
    4. Jan M. Żytkow, Robert Zembowicz
      Pages 323-336

About this book

Introduction

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases.
The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others.
Rough Sets and Data Mining: Analysis of Imprecise Data will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.

Keywords

algorithms artificial intelligence data mining database evolution evolutionary computation fuzzy fuzzy control information intelligence knowledge learning machine learning multi-agent system set theory

Authors and affiliations

  • T. Y. Lin
    • 1
  • N. Cercone
    • 2
  1. 1.San Jose State UniversitySan JoseUSA
  2. 2.University of ReginaReginaCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-1461-5
  • Copyright Information Springer-Verlag US 1997
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8637-0
  • Online ISBN 978-1-4613-1461-5
  • About this book
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