Probabilistic consensus clustering using evidence accumulation

André Lourenço, Samuel Rota Bulò (Corresponding Author), Nicola Rebagliati, Ana L.N. Fred, Mário A.T. Figueiredo, Marcello Pelillo

Research output: Contribution to journalArticleScientificpeer-review

35 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)331-357
JournalMachine Learning
Issue number1-2
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed


  • Bregman divergence
  • consensus clustering
  • ensemble clustering
  • evidence accumulation


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