Automated support specification for efficient mining of interesting association rules

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 constraint. 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 the way for setting the appropriate support constraint, all current approaches leave the users to be 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 not only is good at discovering high-confidence and positive lift associations, but also is effective in reducing the spurious frequent itemsets. W.Y. LIN AND M.C. TSENG 2 Journal of Information Science © CILIP 2005