Full Citation

Title: Further Pruning for Efficient Association Rule Discovery

Citation Type: Conference Paper

Publication Year: 2001

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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%.

Url: https://link.springer.com/chapter/10.1007/3-540-45656-2_52

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Authors: Zhang, Songmao; Webb, Geoffrey, I

Conference Name: Australian Joint Conference on Artificial Intelligence

Publisher Location: Adelaide, S.Aust.

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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