Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy

Abstract

This paper presents some new algorithms to efficiently mine max frequent generalized itemsets (g-itemsets) and essential generalized association rules (g-rules). These are compact and general representations for all frequent patterns and all strong association rules in the generalized environment. Our results fill an important gap among algorithms for frequent patterns and association rules by combining two concepts. First, generalized itemsets employ a taxonomy of items, rather than a flat list of items. This produces more natural frequent itemsets and associations such as (meat, milk) instead of (beef, milk), (chicken, milk), etc. Second, compact representations of frequent itemsets and strong rules, whose result size is exponentially smaller, can solve a standard dilemma in mining patterns: with small threshold values for support and confidence, the user is overwhelmed by the extraordinary number of identified patterns and associations; but with large threshold values, some interesting patterns and associations fail to be identified.