A Parallel Apriori Algorithm for Frequent Itemsets Mining

Authors:
Yanbin Ye;Chia-Chu Chiang
Affiliations:
Acxiom Corporation, Little Rock, Arkansas;University of Arkansas at Little Rock
Venue:
SERA '06 Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications
Year:
2006

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Abstract

Finding frequent itemsets is one of the most investigated fields of data mining. The Apriori algorithm is the most established algorithm for frequent itemsets mining (FIM). Several implementations of the Apriori algorithm have been reported and evaluated. One of the implementations optimizing the data structure with a trie by Bodon catches our attention. The results of the Bodon's implementation for finding frequent itemsets appear to be faster than the ones by Borgelt and Goethals. In this paper, we revised Bodon's implementation into a parallel one where input transactions are read by a parallel computer. The effect a parallel computer on this modified implementation is presented.