Parallel SAT based closed frequent itemsets enumeration

Frequent itemset mining (FIM) is a useful task for discovering frequent co-occurring items. Since its inception, a number of significant FIM algorithms have been developed to speed up mining performances. Unfortunately, for huge dataset, scalability remains an important issue. In this work, we propose a new propositional satisfiability (SAT) parallel approach, called PSATCFIM, to deal with closed frequent itemsets mining problem. It is designed to run on multicore machines and uses a divide and conquer approach to partition the enumeration process. Such partitioning based on guiding paths eliminates computational overlap between cores. Through empirical study, we demonstrate that PSATCFIM can achieve significant performance improvements with respect to the sequential based version.