Abstract: The traditional parallel association rule algorithm defines a MapReduce task for each iteration to implement the generation and counting function of the candidate set,but multiple startup of the MapReduce task brings great performance overhead.This paper defined a parallel association rule mining algorithm (PST-Apriori).This algorithm adopts a partition strategy,defines a prefix shared tree in each distributed computing node,and compresses the candidate items generated by each transaction T to the prefix shared tree (PST).Then the breadth traversal algorithm is used,and the 〈key,value〉 corresponding to each node are used as input of the map function,and the MapReduce frame is automatically gathered according to the key value.Finally,the reduce function is called to aggregate the processing results of multiple tasks,and the frequent itemsets satisfying the minimum support threshold are obtained.The algorithm only usestwo MapReduce tasks,and PST is sorted according to key value to facilitate shuffle operation at Mapper,which improves the efficiency of operation.

Key words: Association rule, Hadoop, MapReduce, Prefix shared tree