Searching frequent itemsets by clustering data: Towards a parallel approach using mapreduce

Abstract

We propose a new algorithm for searching frequent itemsets in large data bases. The idea is to start searching from a set of representative examples instead of testing the 1-itemset,the k-itemset and so on. A clustering algorithm is firstly applied in order to cluster the transactions into k clusters. The set of the k representative examples will be used as the starting point for searching frequent itemsets. Each cluster is represented by the most representative example. We show some preliminary results and we then propose a parallel version of this algorithm based on the MapReduce Framework. © 2013 Springer-Verlag.