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Data Mining, Rough Sets and Granular Computing

  • Tsau Young Lin
  • Yiyu Y. Yao
  • Lotfi A. Zadeh

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

Table of contents

  1. Front Matter
    Pages I-IX
  2. Granular Computing — A New Paradigm

  3. Granular Computing in Data Mining

  4. Data Mining

    1. Front Matter
      Pages 143-143
    2. Jianchao Han, Nick Cercone, Xiaohua Hu
      Pages 145-165
    3. Wenke Lee, Salvatore J. Stolfo, Kui W. Mok
      Pages 166-189
    4. Bing Liu, Yiming Ma, Ching Kian Wong
      Pages 190-215
    5. Balaji Padmanabhan, Alexander Tuzhilin
      Pages 216-231
  5. Granular Computing

    1. Front Matter
      Pages 247-247
    2. Claudi Alsina, Joan Jacas, Enric Trillas
      Pages 249-264
    3. Didier Dubois, Allel Hadj-Ali, Henri Prade
      Pages 290-307
    4. A. Fattah, V. Pouchkarev, A. Belenki, A. Ryjov, L. A. Zadeh
      Pages 308-338
    5. Gaspar Mayor, Adolfo R. de Soto, Jaume Suñer, Enric Trillas
      Pages 350-363
  6. Rough Sets and Granular Computing

    1. Front Matter
      Pages 445-445
    2. Xiaohua Hu, Nick Cercone, Jianchao Han, Wojciech. Ziarko
      Pages 447-460
    3. D. S. Malik, John N. Mordeson
      Pages 461-473
    4. Arul Siromoney, Katsushi Inoue
      Pages 499-517
    5. Hideo Tanaka, Peijun Guo
      Pages 518-536
  7. Back Matter
    Pages 536-536

About this book

Introduction

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.

Keywords

Extension Scoring approximation calculus classification data analysis data mining fuzzy sets knowledge discovery logic logic programming natural language perception programming visualization

Editors and affiliations

  • Tsau Young Lin
    • 1
  • Yiyu Y. Yao
    • 2
  • Lotfi A. Zadeh
    • 3
  1. 1.Department of Mathematics and Computer ScienceSan Jose State University The Metropolitan University of Silicon ValleySan JoseUSA
  2. 2.Department of Computer ScienceUniversity of ReginaReginaCanada
  3. 3.Computer Science Division and Electronics Research Laboratory Department of Electrical and ElectronicsUniversity of California Berkeley Initiative in Soft Computing (BISC)BerkeleyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-7908-1791-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-2508-4
  • Online ISBN 978-3-7908-1791-1
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site
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