Frequent Itemset Mining with Multiple Minimum Supports: a Constraint-based Approach

Discovering relevant patterns for a particular user remains a challenging task in data mining. In real-life applications, relevant patterns may be either frequent or rare ones in the data. In itemset mining, setting the minimum support threshold is a real dilemma (a high value misses rare itemsets, a low value generates a large number of meaningless itemsets). To tackle the rare item problem [8], several approaches were proposed to mine frequent pattern with multiple minimum supports. In [8], the problem of mining frequent itemsets with multiple Minimum Item Supports (MIS) was introduced with a first revision of Apriori algorithm (MSApriori). Then, other Apriori-like approaches were proposed like MMS Cumulate and MMS Stratify [12]. The well-known FPGrowth was extended with a condensed FP-tree structure to mine frequent itemsets with multiple MIS (CFPGrowth [5], CFPGrowth [6]). In [3], FP ME was proposed based on set-enumeration-tree structure and sorted downward closure property. The specialized algorithms introduced previously are effective for mining patterns with multiple MIS.