Mining Rare Association Rules in the Datasets with Widely Varying Items' Frequencies

Rare association rule is an association rule consisting of rare items. It is difficult to mine rare association rules with a simile minimum support (minsup) constraint because low minsup can result in generating too many rules in which some of them can be uninteresting. In the literature, minimum constraint model using "multiple minsup framework" was proposed to efficiently discover rare association rules. However, that model still extracts uninteresting rules if the items' frequencies in a dataset vary widely. In this paper, we exploit the notion of "item-to-pattern difference" and propose multiple minsup based FP-growth-like approach to efficiently discover rare association rules. Experimental results show that the proposed approach is efficient.