Association Rule Mining

Models and Algorithms

  • Chengqi Zhang
  • Shichao Zhang

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2307)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2307)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Pages 1-23
  3. Pages 25-46
  4. Pages 85-120
  5. Pages 121-159
  6. Back Matter
    Pages 229-238

About this book


Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention.
The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.


Algorithmic Learning Association Rule Mining Association Rules Causal Rules Computational Learning Discovery Science Quantitative Associati algorithms data analysis data mining database knowledge knowledge discovery learning machine learning

Editors and affiliations

  • Chengqi Zhang
    • 1
  • Shichao Zhang
    • 1
  1. 1.Faculty of Information TechnologyUniversity of Technology, SydneySydneyAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-43533-4
  • Online ISBN 978-3-540-46027-5
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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