Consensus clustering on data fragments

Abstract

Consensus clustering, also known as clustering ensembles is a technique that combines multiple clustering solutions to obtain stable, accurate and novel results. Over the last years several consensus clustering approaches were proposed addressing practical clustering problems with different degrees of success. In this paper, we consider data fragments as elements of a cluster ensemble framework. We propose a new dissimilarity measure on data fragments and build a consensus function that allows handling large scale clustering problems while not compromising on accuracy. We evaluate our proposed consensus function on a number of datasets showing its high performance with respect to other existing consensus functions.