A parallel algorithm for frequent itemset mining

Frequent itemsets mining plays an essential role in data mining. In this paper, a new algorithm PFP-growth (Parallel FP-growth), which is based on the improved FP-growth, is proposed for parallel frequent itemset mining. The new algorithm distributes the task fairly among the parallel processors. We devise partitioning strategies at different stages of the mining process to achieve balance between processors and adopt some data structure to reduce the information transportation between processors. The experiments on national high performance parallel computer show that the PFP-growth is an efficient parallel algorithm for mining frequent itemset.