A haoop-based parallel mining of frequent itemsets using N-Lists

作者：Sohrabi Mohammad Karim^*; Taheri Narjes

来源：Journal of the Chinese Institute of Engineers, 2018, 41(3): 229-238.

DOI：10.1080/02533839.2018.1454853

• Data mining
• frequent itemset mining
• hadoop
• parallelization

Frequent pattern mining is the most important phase of association rule mining process because of its time and space complexity. Several methods have attempted to improve the performance of association rule mining by enhancing frequent pattern mining efficiency. Due to the large size of the data-sets and huge amounts of data which should be mined, many parallel and distributed mining approaches have been introduced to divide data-sets or to distribute mining processes between multiple processors or computers and thus, improve the efficiency of the mining process. In this paper, we propose a hadoop-based parallel implementation of PrePost+ algorithm for frequent itemset mining. In our parallel approach, the process of constructing N-Lists of itemsets has been distributed between the mappers and the operation of the final pruning process and extracting frequent itemsets has been carried out by reducers in a map-reduce parallel programming model.The experimental results show that our hadoop-based PrePost+(HBPrePost+) algorithm outperforms one of the best existing parallel methods of frequent itemset mining (PARMA) in terms of execution time.

• 出版日期2018