MREclat: An Algorithm for Parallel Mining Frequent Itemsets

Algorithm Eclat is a classical algorithm for mining frequent itemsets, which is based on vertical layout databases. It is greatly different from those algorithms based on horizontal layout databases, such as algorithm Apriori and FP-Growth. In order to improve the efficiency of mining frequent itemsets from massive datasets, parallel algorithm MREclat based on Map/Reduce framework is presented. The algorithm also overcomes the problem of memory and computational capability insufficient when mining frequent itemsets from massive datasets. In this paper, the idea of MREclat is introduced and the performance of the algorithm is studied. The experimental results show that algorithm MREclat has high scalability and good speedup.