IFIN+: A Parallel Incremental Frequent Itemsets Mining in Shared-Memory Environment

In an effort to increase throughput for IFIN, a frequent itemsets mining algo-rithm, in this paper we introduce a solution, called IFIN+, for parallelizing the al-gorithm IFIN with shared-memory multithreads. The inspiration for our motiva-tion 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 environ-ments. Some portions in the serial version are changed in means which increase computational independence for convenience in designing parallel computation with Work-Pool model, be known as a good model for load balance. We con-ducted 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 Pre-Post+. 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 im-proved significantly.