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Link to original content: https://link.springer.com/doi/10.1007/978-1-4613-1461-5
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Rough Sets and Data Mining

Analysis of Imprecise Data

  • Book
  • © 1997

Overview

Authors:
  1. T. Y. Lin

    1. San Jose State University, San Jose, USA

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  2. N. Cercone

    1. University of Regina, Regina, Canada

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    You can also search for this author in PubMed   Google Scholar

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About this book

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.

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Keywords

Table of contents (21 chapters)

  1. Front Matter

    Pages i-xi
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  2. Expositions

    1. Front Matter

      Pages 1-1
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    2. Rough Sets

      • ZdzisÅ‚aw Pawlak
      Pages 3-7

    3. Data Mining: Trends in Research and Development

      • Jitender S. Deogun, Vijay V. Raghavan, Amartya Sarkar, Hayri Sever
      Pages 9-45

    4. A Review of Rough Set Models

      • Y. Y. Yao, S. K. M. Wong, T. Y. Lin
      Pages 47-75

    5. Rough Control: A Perspective

      • Toshinori Munakata
      Pages 77-88

  3. Applications

    1. Front Matter

      Pages 89-89
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    2. Machine Learning & Knowledge Acquisition, Rough Sets, and the English Semantic Code

      • Jerzy W. Grzymala-Busse, Sally Yeates Sedelow, Walter A. Sedelow Jr.
      Pages 91-107

    3. Generation of Multiple Knowledge from Databases Based on Rough Sets Theory

      • Xiaohua Hu, Nick Cercone, Wojciech Ziarko
      Pages 109-121

    4. Fuzzy Controllers: An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing

      • T. Y. Lin
      Pages 123-138

    5. Rough Real Functions and Rough Controllers

      • ZdzisÅ‚aw Pawlak
      Pages 139-147

    6. A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms for Hybrid Diagnostic Systems

      • Ray R. Hashemi, Bruce A. Pearce, Ramin B. Arani, Willam G. Hinson, Merle G. Paule
      Pages 149-175

    7. Rough Sets as a Tool for Studying Attribute Dependencies in the Urinary Stones Treatment Data Set

      • Jerzy Stefanowski, Krzysztof SÅ‚owiÅ„ski
      Pages 177-196

  4. Related Areas

    1. Front Matter

      Pages 197-197
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    2. Data Mining Using Attribute-Oriented Generalization and Information Reduction

      • Nick J. Cercone, Howard J. Hamilton, Xiaohua Hu, Ning Shan
      Pages 199-227

    3. Neighborhoods, Rough Sets, and Query Relaxation in Cooperative Answering

      • James B. Michael, T. Y. Lin
      Pages 229-238

    4. Resolving Queries Through Cooperation in Multi-Agent Systems

      • Zbigniew W. Ras
      Pages 239-258

    5. Synthesis of Decision Systems from Data Tables

      • Andrzej Skowron, Lech Polkowski
      Pages 259-299

    6. Combination of Rough and Fuzzy Sets Based on α-Level Sets

      • Y. Y. Yao
      Pages 301-321

    7. Theories that Combine Many Equivalence and Subset Relations

      • Jan M. Å»ytkow, Robert Zembowicz
      Pages 323-336
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Authors and Affiliations

  • San Jose State University, San Jose, USA

    T. Y. Lin

  • University of Regina, Regina, Canada

    N. Cercone

Bibliographic Information

  • Book Title: Rough Sets and Data Mining

  • Book Subtitle: Analysis of Imprecise Data

  • Authors: T. Y. Lin, N. Cercone

  • DOI: https://doi.org/10.1007/978-1-4613-1461-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Kluwer Academic Publishers 1997

  • Hardcover ISBN: 978-0-7923-9807-3Published: 30 November 1996

  • Softcover ISBN: 978-1-4612-8637-0Published: 02 October 2011

  • eBook ISBN: 978-1-4613-1461-5Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XII, 436

  • Topics: Artificial Intelligence, Data Structures and Information Theory, Mathematical Logic and Foundations

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