Mining top-k strongly correlated item pairs without minimum correlation threshold

Authors:
Zengyou He;Xiaofei Xu;Shengchun Deng
Affiliations:
Department of Computer Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, P.O Box 315, P. R. China, 150001;Department of Computer Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, P.O Box 315, P. R. China, 150001;Department of Computer Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, P.O Box 315, P. R. China, 150001
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems
Year:
2006

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Abstract

Given a user-specified minimum correlation threshold and a transaction database, the problem of mining strongly correlated item pairs is to find all item pairs with Pearson's correlation coefficients above the threshold. However, setting such a threshold is by no means an easy task. In this paper, we consider a more practical problem: mining top-k strongly correlated item pairs, where k is the desired number of item pairs that have largest correlation values. Based on the FP-tree data structure, we propose an efficient algorithm, called Tkcp, for mining such patterns without minimum correlation threshold. Our experimental results show that Tkcp algorithm outperforms the Taper algorithm, one efficient algorithm for mining correlated item pairs, even with the assumption of an optimally chosen correlation threshold. Thus, we conclude that mining top-k strongly correlated pairs without minimum correlation threshold is more preferable than the original correlation threshold based mining.