On Efficiency of Dataset Filtering Implementations in Constraint-Based Discovery of Frequent Itemsets

Discovery of frequent itemsets is one of the fundamental data mining problems. Typically, the goal is to discover all the itemsets whose support in the source dataset exceeds a user-specified threshold. However, very often users want to restrict the set of frequent itemsets to be discovered by adding extra constraints on size and contents of the itemsets. Many constraint-based frequent itemset discovery techniques have been proposed recently. One of the techniques, called dataset filtering, is based on the observation that for some classes of constraints, itemsets satisfying them can only be supported by transactions that satisfy the same constraints. Conceptually, dataset filtering transforms a given data mining task into an equivalent one operating on a smaller dataset. In this paper we discuss possible implementations of dataset filtering, evaluating their strengths and weaknesses.