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Information Fusion in Data Mining

  • Vicenç Torra

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 123)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Trends in Information fusion in Data Mining

  3. Aggregation Operators: Methods and Properties

  4. Preprocessing Data

  5. Model Building

    1. Front Matter
      Pages 133-133
    2. Michel Grabisch
      Pages 135-148
    3. Hideyuki Imai, Daiki Asano, Yoshiharu Sato
      Pages 149-159
    4. Yukinobu Hamuro, Naoki Katoh, Ip H. Edward, Stephane L. Cheung, Katsutoshi Yada
      Pages 161-187
    5. Tomoharu Nakashima, Gaku Nakai
      Pages 189-208
  6. Information Extraction

    1. Front Matter
      Pages 209-209
    2. Ronald R. Yager
      Pages 211-229
  7. Back Matter
    Pages 231-233

About this book

Introduction

Information fusion is becoming a major need in data mining and knowledge discovery in databases. This book presents some recent fusion techniques that are currently in use in data mining, as well as data mining applications that use information fusion. Special focus of the book is on information fusion in preprocessing, model building and information extraction with various applications.

Keywords

Fusion Techniques Information Extraction Model Building Preprocessing algorithm algorithms calculus classification data mining database databases fuzzy model modeling operator

Editors and affiliations

  • Vicenç Torra
    • 1
  1. 1.Institut d’Investigació en Intel·ligència Artificial CSIC - Spanish Scientific Research CouncilCampus Universitat Autònoma de BarcelonaBellaterra, CataloniaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-36519-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-05628-4
  • Online ISBN 978-3-540-36519-8
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site