The cleaning method of duplicate big data based on association rule mining algorithm

Abstract

In order to overcome the problems of low cleaning efficiency and serious memory consumption in traditional large data cleaning methods, this paper proposes a new cleaning method of repeated big data based on association rule mining algorithm. This method uses association rule mining algorithm to obtain the frequent itemsets of repeated big data after repeated cycle calculation. At the same time, the output mode of the algorithm is optimised in parallel, and the Hadoop interface is modified to change the reading mode of MapReduce. The first frequent itemset is used to clean the repeated big data. The experimental results show that the proposed method can effectively reduce the execution time and memory consumption, and the shortest cleaning time is only 1.28 min, indicating the feasibility of the proposed method.