Learning Similarities by Accumulating Evidence in a Probabilistic Way

Abstract

Clustering ensembles take advantage of the diversity produced by multiple clustering algorithms to produce a consensual partition. Evidence accumulation clustering (EAC) combines the output of a clustering ensemble into a co-association similarity matrix, which contains the co-occurrences between pairs of objects in a cluster. A consensus partition is then obtained by applying a clustering technique over this matrix. We propose a new combination matrix, where the co-occurrences between objects are modeled in a probabilistic way. We evaluate the proposed methodology using the dissimilarity increments distribution model. This distribution is based on a high-order dissimilarity measure, which uses triplets of nearest neighbors to identify sparse and odd shaped clusters. Experimental results show that the new proposed algorithm produces better and more robust results than EAC in both synthetic and real datasets.