Ensemble clustering based on evidence theory

Abstract

Ensemble clustering consists in combining multiple clustering solutions into a single one, called the consensus, which can produce a more accurate and robust clustering of the data. In this paper, we attempt to implement ensemble clustering using Dempster-Shafer evidence theory. Individual clustering solutions are obtained using evidence theory and a novel diversity measure is proposed using the distance of evidence for selecting complementary individual solutions. After establishing the correspondence among different clustering solutions' labels, the consensus clustering solution can be obtained using evidence combination. Experimental results and related analyses show that our proposed approach can effectively implement the ensemble clustering.