Further pruning for efficient association rule discovery

Songmao Zhang, Geoffrey Ian Webb

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


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%.
Original languageEnglish
Title of host publicationAI 2001: Advances in Artificial Intelligence
Subtitle of host publication14th Australian Joint Conference on Artificial Intelligence Adelaide, Australia, December 10-14, 2001 Proceedings
EditorsMarkus Stumptner, Dan Corbett, Mike Brooks
Place of PublicationBerlin Germany
Number of pages14
ISBN (Print)3540429603
Publication statusPublished - 2001
Externally publishedYes
EventAustralasian Joint Conference on Artificial Intelligence 2001 - Adelaide, Australia
Duration: 10 Dec 200114 Dec 2001
Conference number: 14th
https://link.springer.com/book/10.1007/3-540-45656-2 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceAustralasian Joint Conference on Artificial Intelligence 2001
Abbreviated titleAI 2001
Internet address

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