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Proceedings Paper

Efficient mining of strongly correlated item pairs
Author(s): Shuxin Li; Robert Lee; Sheau-Dong Lang
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Paper Abstract

Past attempts to mine transactional databases for strongly correlated item pairs have been beset by difficulties. In an attempt to be efficient, some algorithms produce false positive and false negative results. In an attempt to be accurate and comprehensive, other algorithms sacrifice efficiency. We propose an efficient new algorithm that uses Jaccard's correlation coefficient, which is simply the ratio between the sizes of the intersection and the union of two sets, to generate a set of strongly correlated item pairs that is both accurate and comprehensive. The pruning of candidate item pairs based on an upper bound facilitates efficiency. Furthermore, there is no possibility of false positives or false negatives. Testing of our algorithm on datasets of various sizes shows its effectiveness in real-world application.

Paper Details

Date Published: 18 April 2006
PDF: 11 pages
Proc. SPIE 6241, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006, 624102 (18 April 2006); doi: 10.1117/12.664567
Show Author Affiliations
Shuxin Li, Univ. of Central Florida (United States)
Robert Lee, Univ. of Central Florida (United States)
Sheau-Dong Lang, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 6241:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006
Belur V. Dasarathy, Editor(s)

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