A fast and effective partitioning algorithm for document clustering

Abstract

Fast and high quality document clustering is one of the most important tasks in the modern era of information. With the huge amount of available data and with an aim to creating better quality clusters, scores of algorithms having quality-complexity trade-offs have been proposed. Some of the proposed algorithms attempt to minimize the computational overload in terms of certain criterion functions defined for the whole set of clustering solution. In this paper, we have proposed a novel algorithm for document clustering using a graph based criterion function. Our algorithm is partitioning in nature. Most of the commonly used partitioning clustering algorithms are inflicted with the drawback of trapping into local optimum solutions. However, the algorithm proposed in this paper usually leads to the global optimum solution. Its performance enhances with the increment in the number of clusters. We have carried out sophisticated experiments wherein we have compared our algorithm with two well known document clustering algorithms viz. k-means and k-means++ algorithm. The results so obtained confirm the superiority of our algorithm. © 2012 Springer-Verlag.