Parallel Hierarchical Association Rule Mining in Big Data Environment
Zhongli Zhang · 2016
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 Dinto 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.