A new efficient approach in clustering ensembles

Abstract

Previous clustering ensemble algorithms usually use a consensus function to obtain a final partition from the outputs of the initial clustering. In this paper, we propose a new clustering ensemble method, which generates a new feature space from initial clustering outputs. Multiple runs of an initial clustering algorithm like k-means generate a new feature space, which is significantly better than pure or normalized feature space. Therefore, running a simple clustering algorithm on generated feature space can obtain the final partition significantly better than pure data. In this method, we use a modification of k-means for initial clustering runs named as "Intelligent kmeans", which is especially defined for clustering ensembles. The results of the proposed method are presented using both simple k-means and intelligent kmeans. Fast convergence and appropriate behavior are the most interesting points of the proposed method. Experimental results on real data sets show effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2007.