IFIN+: A parallel incremental frequent itemsets mining in shared-memory environment

Abstract

In an effort to increase throughput for IFIN, a frequent itemsets mining algorithm, in this paper we introduce a solution, called IFIN+, for parallelizing the algorithm IFIN with shared-memory multithreads. The inspiration for our motivation is that today commodity processors’ computational power is enhanced with multi physical computational units; and therefore, exploiting full advantage of this is a potential solution for improving performance in single-machine environments. Some portions in the serial version are changed in means which increase efficiency and computational independence for convenience in designing parallel computation with Work-Pool model, be known as a good model for load balance. We conducted experiments to evaluate IFIN+ against its serial version IFIN, the well-known algorithm FP-Growth and other two state-of-the-art ones FIN and PrePost+. The experimental results show that the running time of IFIN+ is the most efficient, especially in the case of mining at different support thresholds in the same running session. Compare to its serial version, IFIN+ performance is improved significantly.