Abstract: Frequent Itemset Mining is one of the classical data mining problems in most of the data mining applications. There are various parallel mining algorithms available for frequent itemsets mining, such as Apriori, Fp-Growth algorithms. However, these parallel mining algorithms lack features like automated parallelization, fine load balancing, and distribution of data on large clusters. To address these issues the most effective recent method is using the enhanced version of Apriori algorithm (EA). In this technique three MapReduce tasks are implemented to complete the mining of big datasets by using the parallelism among computing nodes of clusters to improve the performance of frequent pattern mining on hadoop clusters. After third map reduce job, frequent patterns will be produced as a final outcome.

Keywords: frequent item sets, Enhanced Apriori, Hadoop, MapReduce.