Generating Frequent Itemsets by RElim on Hadoop Clusters

Abstract

Data mining is considered as the process of extracting the useful information by finding the hidden information out of large chunks of dataset. Frequent itemset mining is the popular data mining methods. MapReduce has turn out to be an important distributed processing model for large-scale data-intensive applications like data mining. MapReduce is an efficient, scalable, and easy programming model for large-scale distributed data processing on a huge cluster of commodity computers. In this paper, RElim algorithm is implemented on MapReduce framework.