Mining with constraints by pruning and avoiding ineffectual processing

Authors:
Mohammad El-Hajj;Osmar R. Zaïane
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, AB, Canada;Department of Computing Science, University of Alberta, Edmonton, AB, Canada
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 3
Cited 1

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Abstract

It is known that algorithms for discovering association rules generate an overwhelming number of those rules. While many new very efficient algorithms were recently proposed to allow the mining of extremely large datasets, the problem due to the sheer number of rules discovered still remains. In this paper we propose a new way of pushing the constraints in dual-mode based from the set of maximal patterns that is an order of magnitude smaller than the set of all frequent patterns.