Center matching scheme for k-means cluster ensembles

Abstract

ABSTRACT In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster ensemble learning. Each k-means clusterer outputs a sequence w ith k cluster centers. We randomly select a cluster center sequence as a reference one, and then we rearrange the other cluster center sequences according to the reference sequence. Then we label the data using these matched cluster center sequences. Hence we get multiple partitions or clusterings. Finally, multiple clusterings are combined to the best labeling by using combination rules, such as the majority voting rule, the weighted voting rule and the selectiv e weighted voting rule. Experimental results on 7 UCI data sets show that our ensemble methods could improve the clustering results effectively. Keywords: unsupervised learning, cluster ensembles, k-means clustering 1. INTRODUCTION Cluster ensembles have been demonstrated the advantages over single clusterers in pattern recognition and data mining