Cluster Ensemble Based on Iteratively Refined Co-Association Matrix

Abstract

Cluster ensemble aims at discovering the intrinsic structure of a given dataset robustly and stably, and achieves this by combining multiple base partitions into a single final one. Some cluster ensemble algorithms in the literature are based on a co-association matrix, which can be viewed as a space transformation of the original dataset. However, the co-association matrix does not always depict the cluster structure well. In this paper, we propose a method to refine the co-association matrix and make it describe the structure more accurately. The main idea is to define an inter-cluster similarity with the co-association matrix, then repeatedly combine the most similar cluster pair of a base partition. In turn, the co-association matrix is updated in terms of the combined cluster pair. Furthermore, based on the refined co-association matrix, three consensus schemes are designed to generate the final clustering. The experimental results on eight synthetic datasets and eight real datasets demonstrate that the refined co-association matrix depicts the cluster structure more accurate than the original one, and the proposed ensemble schemes with the refined matrix can produce clusterings with high quality compared with the several state-of-the-art methods.