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Pairwise Probabilistic Clustering Using Evidence Accumulation

  • Samuel Rota Bulò
  • André Lourenço
  • Ana Fred
  • Marcello Pelillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)

Abstract

In this paper we propose a new approach for consensus clustering which is built upon the evidence accumulation framework. Our method takes the co-association matrix as the only input and produces a soft partition of the dataset, where each object is probabilistically assigned to a cluster, as output. Our method reduces the clustering problem to a polynomial optimization in probability domain, which is attacked by means of the Baum-Eagon inequality. Experiments on both synthetic and real benchmarks data, assess the effectiveness of our approach.

Keywords

Average Link Cluster Problem Single Link Cluster Assignment Cluster Ensemble 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Samuel Rota Bulò
    • 1
  • André Lourenço
    • 3
  • Ana Fred
    • 2
    • 3
  • Marcello Pelillo
    • 1
  1. 1.Dipartimento di InformaticaUniversity of VeniceItaly
  2. 2.Instituto Superior TécnicoLisbonPortugal
  3. 3.Instituto de TelecomunicaçõesLisbonPortugal

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