Further pruning for efficient association rule discovery

Zhang, Songmao and Webb, Geoffrey I. 2001, Further pruning for efficient association rule discovery, in AI 2001 : Advances in Artificial Intelligence : Proceedings of the 14th Australian Joint Conference on Artificial Intelligence, [The Conference], [Adelaide, S.Aust.], pp. 605-618.

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Title Further pruning for efficient association rule discovery
Author(s) Zhang, Songmao
Webb, Geoffrey I.
Conference name Australian Joint Conference on Artificial Intelligence (14th : 2001 : Adelaide)
Conference location Adelaide, S.Aust.
Conference dates 10-14 Dec. 2001
Title of proceedings AI 2001 : Advances in Artificial Intelligence : Proceedings of the 14th Australian Joint Conference on Artificial Intelligence
Editor(s) Corbett, Dan
Brooks, Mike
Stumpter, Markus
Publication date 2001
Conference series Australian Joint Conference on Artificial Intelligence
Start page 605
End page 618
Publisher [The Conference]
Place of publication [Adelaide, S.Aust.]
Keyword(s) machine learning
search
Summary 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%.
ISBN 03540429603
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2001, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004544

Document type: Conference Paper
Collection: School of Information Technology
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