Optimization of initial centroids for k-means algorithm based on small world network

Abstract

K-means algorithm is a relatively simple and fast gather clustering algorithm. However, the initial clustering center of the traditional k-means algorithm was generated randomly from the dataset, and the clustering result was unstable. In this paper, we propose a novel method to optimize the selection of initial centroids for k-means algorithm based on the small world network. This paper firstly models a text document set as a network which has small world phenomenon and then use small-world's characteristics to form k initial centroids. Experimental evaluation on documents croups show clustering results (total cohesion, purity, recall) obtained by proposed method comparable with traditional k-means algorithm. The experiments show that results are obtained by the proposed algorithm can be relatively stability and efficiency. Therefore, this method can be considered as an effective application in the domain of text documents, especially in using text clustering for topic detection. © 2012 IFIP International Federation for Information Processing.