Partition-Based Approach to Processing Batches of Frequent Itemset Queries

We consider the problem of optimizing processing of batches of frequent itemset queries. The problem is a particular case of multiple-query optimization, where the goal is to minimize the total execution time of the set of queries. We propose an algorithm that is a combination of the Mine Merge method, previously proposed for processing of batches of frequent itemset queries, and the Partition algorithm for memory-based frequent itemset mining. The experiments show that the novel approach outperforms the original Mine Merge and sequential processing in majority of cases.