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Finding frequent itemsets is one of the most important fields of data mining. Apriori algorithm is the most established algorithm for finding frequent itemsets from a transactional dataset; however, it needs to scan the dataset many times and to generate many candidate itemsets. In this paper, the authors have implemented an efficient MapReduce Apriori algorithm (MRApriori) based on Hadoop-MapReduce model which needs only two phases (MapReduce Jobs) to find all frequent k-itemsets, and compared their proposed MRApriori algorithm with current two existed algorithms which need either one or k phases (k is maximum length of frequent itemsets) to find the same frequent k-itemsets. Experimental results showed that the proposed MRApriori algorithm outperforms the other two algorithms.
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