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Data Mining, Rough Sets and Granular Computing

  • Book
  • © 2002

Overview

Editors:
  1. Tsau Young Lin

    1. Department of Mathematics and Computer Science, San Jose State University The Metropolitan University of Silicon Valley, San Jose, USA

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  2. Yiyu Y. Yao

    1. Department of Computer Science, University of Regina, Regina, Canada

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  3. Lotfi A. Zadeh

    1. Computer Science Division and Electronics Research Laboratory Department of Electrical and Electronics, University of California Berkeley Initiative in Soft Computing (BISC), Berkeley, USA

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  • The book integrates data mining, rough sets and granular computing
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 95)

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Table of contents (26 chapters)

  1. Front Matter

    Pages I-IX
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  4. Data Mining

    1. Front Matter

      Pages 143-143
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    2. An Interactive Visualization System for Mining Association Rules

      • Jianchao Han, Nick Cercone, Xiaohua Hu
      Pages 145-165

    3. Algorithms for Mining System Audit Data

      • Wenke Lee, Salvatore J. Stolfo, Kui W. Mok
      Pages 166-189

    4. Scoring and Ranking the Data Using Association Rules

      • Bing Liu, Yiming Ma, Ching Kian Wong
      Pages 190-215

    5. Finding Unexpected Patterns in Data

      • Balaji Padmanabhan, Alexander Tuzhilin
      Pages 216-231

    6. Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model

      • Shusaku Tsumoto
      Pages 232-246

  5. Granular Computing

    1. Front Matter

      Pages 247-247
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    2. Observability and the Case of Probability

      • Claudi Alsina, Joan Jacas, Enric Trillas
      Pages 249-264

    3. Granulation and Granularity via Conceptual Structures: A Perspective From the Point of View of Fuzzy Concept Lattices

      • Radim BÄ›lohlávek
      Pages 265-289

    4. Granular Computing with Closeness and Negligibility Relations

      • Didier Dubois, Allel Hadj-Ali, Henri Prade
      Pages 290-307

    5. Application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty

      • A. Fattah, V. Pouchkarev, A. Belenki, A. Ryjov, L. A. Zadeh
      Pages 308-338
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Keywords

About this book

During the past few years, data mining has grown rapidly in visibility and importance within information processing and decision analysis. This is par­ ticularly true in the realm of e-commerce, where data mining is moving from a "nice-to-have" to a "must-have" status. In a different though related context, a new computing methodology called granular computing is emerging as a powerful tool for the conception, analysis and design of information/intelligent systems. In essence, data mining deals with summarization of information which is resident in large data sets, while granular computing plays a key role in the summarization process by draw­ ing together points (objects) which are related through similarity, proximity or functionality. In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a branch of granular computing. However, its applications to data mining have predated that of granular computing.

Editors and Affiliations

  • Department of Mathematics and Computer Science, San Jose State University The Metropolitan University of Silicon Valley, San Jose, USA

    Tsau Young Lin

  • Department of Computer Science, University of Regina, Regina, Canada

    Yiyu Y. Yao

  • Computer Science Division and Electronics Research Laboratory Department of Electrical and Electronics, University of California Berkeley Initiative in Soft Computing (BISC), Berkeley, USA

    Lotfi A. Zadeh

Bibliographic Information

  • Book Title: Data Mining, Rough Sets and Granular Computing

  • Editors: Tsau Young Lin, Yiyu Y. Yao, Lotfi A. Zadeh

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-7908-1791-1

  • Publisher: Physica Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2002

  • Hardcover ISBN: 978-3-7908-1461-3Published: 10 April 2002

  • Softcover ISBN: 978-3-7908-2508-4Published: 21 October 2010

  • eBook ISBN: 978-3-7908-1791-1Published: 11 November 2013

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: IX, 537

  • Topics: Artificial Intelligence, Database Management

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