Abstract: To deal with big data’s demand of real-time processing,we proposed the parallel hierarchical association rule mining algorithm based on partitioning.First,the algorithm divides the transactions of D into n nonoverlapping partitions randomly,and all the local frequent itemsets mining is parallelized.Second,apriori property is utilized to collect frequent itemsets from all partitions and form the global candidate itemsets with respect to D.Then the actual support of each candidate is counted to determine the global frequent itemsets.At last,the algorithm’s high efficiency was analyzed by modeling.

Key words: Big data,Partition,Association rule,Parallel hierarchical mining,High efficiency