A framework for mining association rules

Authors:
Jun Luo;Sanguthevar Rajasekaran
Affiliations:
Department of ECCS, Ohio Northern University, Ada, OH;Department of CSE, University of Connecticut, CT
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

Citing 5
Cited 0

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Abstract

Association rule mining is one of important data mining problems. In this paper, a framework for efficiently calculating frequent itemsets in voluminous data is presented. The algorithm FIT [LR] is a practical implemention of the framework. A theoretical comparison between FIT and Eclat [ZPOW] is also explored. The analysis asserts that the performance of FIT is much more efficient than that of Eclat. Experimental results confirmed the assertion with data from [AS].