Robust Clustering Algorithms Based on Finite Mixtures of Multivariate t Distribution

  • Chengwen Yu
  • Qianjin Zhang
  • Lei Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


Providing protection against outlier in clustering data is a difficult problem. We proposed two robust clustering algorithms which integrate two modified versions of EM algorithm for mixtures t model with a model selection criterion respectively. The proposed methods can select the number of clusters component automatically by a combined component annihilation strategy and can also avoid the drawbacks of traditional mixture-based clustering algorithms – highly dependent on initialization and may converge to the boundary of the parameter space [7]. Experiment results show the contrast among different algorithms and demonstrate the effectiveness of our algorithms.


Finite Mixture Finite Mixture Model Mixture Normal Model Deterministic Annealing Minimum Message Length 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chengwen Yu
    • 1
  • Qianjin Zhang
    • 1
  • Lei Guo
    • 1
  1. 1.College of Automatic ControlNorthwestern Polytechnical UniversityXi’anChina

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