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Direct and Incremental Computing of Maximal Covering Rules

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Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

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Abstract

In the paper we consider the knowledge in the form of association rules. The consequents derivable from the given set of association rules constitute the theory for this rule set. We apply maximal covering rules as a concise representation of the theory. We prove that maximal covering rules have precisely computable values of support and confidence, though the theory can contain rules for which these values can be only estimated. Efficient methods of direct and incremental computation of maximal covering rules are offered.

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Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz

Authors
  1. Marzena Kryszkiewicz
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Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Kryszkiewicz, M. (2001). Direct and Incremental Computing of Maximal Covering Rules. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_42

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  • DOI: https://doi.org/10.1007/3-540-45357-1_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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