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)


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.


Average Link Cluster Problem Single Link Cluster Assignment Cluster Ensemble 
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  1. 1.
    Fred, A., Jain, A.K.: Combining multiple clustering using evidence accumulation. IEEE Trans. Pattern Anal. Machine Intell. 27(6), 835–850 (2005)CrossRefGoogle Scholar
  2. 2.
    Jardine, N., Sibson, R.: The construction of hierarchic and non-hierarchic classifications. Computer J. 11, 177–184 (1968)zbMATHGoogle Scholar
  3. 3.
    Banerjee, A., Krumpelman, C., Basu, S., Mooney, R.J., Ghosh, J.: Model-based overlapping clustering. In: Int. Conf. on Knowledge Discovery and Data Mining, pp. 532–537 (2005)Google Scholar
  4. 4.
    Heller, K., Ghahramani, Z.: A nonparametric bayesian approach to modeling overlapping clusters. In: Int. Conf. AI and Statistics (2007)Google Scholar
  5. 5.
    Zass, R., Shashua, A.: A unifying approach to hard and probabilistic clustering. In: Int. Conf. Comp. Vision (ICCV), vol. 1, pp. 294–301 (2005)Google Scholar
  6. 6.
    Baum, L.E., Eagon, J.A.: An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bull. Amer. Math. Soc. 73, 360–363 (1967)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Fred, A., Jain, A.K.: Learning pairwise similarity for data clustering. In: Int. Conf. Patt. Recogn. (ICPR), pp. 925–928 (2006)Google Scholar
  8. 8.
    Fred, A., Jain, A.K.: Data clustering using evidence accumulation. In: Int. Conf. Patt. Recogn. (ICPR), pp. 276–280 (2002)Google Scholar
  9. 9.
    Zass, R., Shashua, A.: Doubly stochastic normalization for spectral clustering. In: Adv. in Neural Inform. Proces. Syst (NIPS), vol. 19, pp. 1569–1576 (2006)Google Scholar
  10. 10.
    Baum, L.E., Sell, G.R.: Growth transformations for functions on manifolds. Pacific J. Math. 27, 221–227 (1968)MathSciNetGoogle Scholar
  11. 11.
    Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statistics 41, 164–171 (1970)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Jain, A.K., Dubes, R.C.: Algorithms for data clustering. Prentice-Hall, Englewood Cliffs (1988)zbMATHGoogle Scholar

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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