Parallel Design of Apriori Algorithm Based on the Method of “Determine Infrequent Items & Remove Infrequent Itemsets”

Abstract In the method of fault association rule diagnosis, Apriori algorithm has low efficiency for big data processing. In this paper, aiming at the defects of Apriori algorithm, MapReduce computing framework is used to optimize the Apriori association rule algorithm. This method improves the accuracy of association mining in fault diagnosis. In the process of optimization, this paper proposes the method of “Determine Infrequent Items & Remove Infrequent Itemsets”. Through experiments, this method effectively reduces the computational space needed by Apriori algorithm in association rule mining, and improves the computing speed.