Full Citation

Title: Automated Support Specification for Efficient Mining of Interesting Association Rules

Citation Type: Journal Article

Publication Year: 2006

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Abstract: In recent years, the weakness of the canonical support-confidence framework for associations mining has been widely studied. One of the difficulties in applying association rules mining is the setting of support constraints. A high-support constraint avoids the combinatorial explosion in discovering frequent itemsets, but at the expense of missing interesting patterns of low support. Instead of seeking a way to set the appropriate support constraints, all current approaches leave the users in charge of the support setting, which, however, puts the users in a dilemma. This paper is an effort to answer this long-standing open question. According to the notion of confidence and lift measures, we propose an automatic support specification for efficiently mining high-confidence and positive-lift associations without consulting the users. Experimental results show that the proposed method is not only good at discovering high-confidence and positive-lift associations, but also effective in reducing spurious frequent itemsets.

User Submitted?: No

Authors: Lin, Wen-Yang; Tseng, Ming-Cheng

Periodical (Full): Journal of Information Science

Issue: 3

Volume: 32

Pages: 238-250

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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