A Novel Clustering Ensemble Method Based on One-Class Support Vector Machine

Abstract

A clustering algorithm based on one-class support vector machine has been proposed recently. Because the kernel technique is used, this approach can appear preferable to the traditional k-means clustering. Clustering ensemble method can combine several divisions of all unlabeled data into a single clustering to gain the better clustering results. In this paper, the clustering ensemble method is applied to the clustering algorithm based one-class support vector machines. Several partitions of multiple runs with different random initial data sets are combined into a final clustering result. Experiments show that the new approach can improve the clustering performance.