Mining Frequent Patterns with Item, Aggregation, and Cardinality Constraints

Recently, the topic of constraint based association mining has received increasing attention within the data mining research community. By allowing more user specified constraints other than traditional rule measurements, e.g., minimum support and confidence, research work on this topic endeavor to reflect real interest of analysts and relief them from the overabundance of rules, and ultimately, fulfill an interactive environment for association analysis. So far most work on constraint based frequent patterns (itemsets) mining has been single constraint oriented, i.e., only one specific type of constraint is considered. Surprisingly little research has been conducted to deal with multiple types of constraints. This paper is an investigation on this problem. Specifically, three types of constraints are considered, including item constraint, aggregation constraint, and cardinality constraint. We propose an efficient algorithm, MCFP (Multi Constrained Frequent Pattern mining) to accomplish the task of discovering frequent itemsets satisfying all three types of constraints. Experimental results show that our algorithm is significantly faster than the intuitive approach, post processing the generated frequent patterns against user specified constraints.