Association Rule Mining Models and Algorithms
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
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Berlin Heidelberg New York Barcelona Hong Kong London Milan Paris Tokyo
Chengqi Zhang Shichao Zhang
Association Rule Mining Models and Algorithms
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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA J¨org Siekmann, University of Saarland, Saarbr¨ucken, Germany Authors Chengqi Zhang Shichao Zhang University of Technology, Sydney, Faculty of Information Technology P.O. Box 123 Broadway, Sydney, NSW 2007 Australia E-mail: {chengqi,zhangsc}@it.uts.edu.au
Cataloging-in-Publication Data applied for Die Deutsche Bibliothek - CIP-Einheitsaufnahme Zhang, Chengqi: Association rule mining : models and algorithms / Chengqi Zhang ; Shichao Zhang. - Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong ; London ; Milan ; Paris ; Tokyo : Springer, 2002 (Lecture notes in computer science ; Vol. 2307 : Lecture notes in artificial intelligence) ISBN 3-540-43533-6
CR Subject Classification (1998): I.2.6, I.2, H.2.8, H.2, H.3, F.2.2 ISSN 0302-9743 ISBN 3-540-43533-6 Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer-Verlag Berlin Heidelberg New York a member of BertelsmannSpringer Science+Business Media GmbH http://www.springer.de © Springer-Verlag Berlin Heidelberg 2002 Printed in Germany Typesetting: Camera-ready by author, data conversion by Boller Mediendesign Printed on acid-free paper SPIN: 10846539 06/3142 543210
Preface
Association rule mining is receiving increasing attention. Its appeal is due, not only to the popularity of its parent topic ‘knowledge discovery in databases and data mining’, but also to its neat representation and understandability. The development of association rule mining has been encouraged by active discussion among communities of users and researchers. All have contributed to the formation of the technique with a fertile exchange of ideas at important forums or conferences, including SIGMOD, SIGKDD, AAAI, IJCAI, and VLDB. Thus association rule mining has advanced into a mature stage, supporting diverse applications such as data analysis and predictive decisions. There has been considerable progress made recently on mining in such areas as quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. These continue to be future topics of interest concerning association rule mining. Though the association rule constitutes an important pattern within databases, to dat
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