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Further Pruning for Efficient Association Rule Discovery

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Book cover AI 2001: Advances in Artificial Intelligence (AI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2256))

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Abstract

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS_AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS_AR. We demonstrate that application of OPUS_AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

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Authors and Affiliations

  1. School of Computing and Mathematics, Deakin University, 3217, Geelong, Victoria, Australia

    Songmao Zhang & Geoffrey I. Webb

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  1. Songmao Zhang
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  2. Geoffrey I. Webb
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Editor information

Editors and Affiliations

  1. School of Computer and Information Science, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Markus Stumptner  & Dan Corbett  & 

  2. Department of Computer Science, University of Adelaide, 5001, Adelaide, SA, Australia

    Mike Brooks

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© 2001 Springer-Verlag Berlin Heidelberg

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Zhang, S., Webb, G.I. (2001). Further Pruning for Efficient Association Rule Discovery. In: Stumptner, M., Corbett, D., Brooks, M. (eds) AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science(), vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_52

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  • DOI: https://doi.org/10.1007/3-540-45656-2_52

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42960-9

  • Online ISBN: 978-3-540-45656-8

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