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Orientador(es)
Resumo(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.
Descrição
Palavras-chave
Consensus clustering Evidence Accumulation Ensemble clustering Bregman divergence
Contexto Educativo
Citação
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
Editora
Springer