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Advisor(s)
Abstract(s)
Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.
Description
Keywords
Consensus clustering Evidence Accumulation Ensemble clustering Bregman divergence
Citation
LOURENÇO, André; [et al.] - Probabilistic consensus clustering using evidence accumulation. Machine Learning. ISSN. 0885-6125. Vol. 98, N.º 1-2, SI (2015), pp. 331-357
Publisher
Springer