An Analysis of Distributed Document Clustering Using MapReduce Based K-Means Algorithm

Abstract

Clustering is considered as one of the important data mining techniques. Document clustering is among many applications of clustering. The traditional clustering algorithms are proven inefficient for clustering rapidly generating large real world datasets. As a solution, traditional clustering algorithms are modified using distributed programming paradigm. MapReduce is a popular distributed programming paradigm designed for Hadoop distributed framework. This paper demonstrates a MapReduce based modification of K-Means clustering algorithm for document datasets. The result shows that the proposed algorithm is efficient than traditional K-Means for all size of document datasets clustering. The experiments also show that the MapReduce clustering works more efficiently when the dataset size and Hadoop cluster sizes are large.