Block clustering with Bernoulli mixture models: Comparison of different approaches

  title={Block clustering with Bernoulli mixture models: Comparison of different approaches},
  author={G{\'e}rard Govaert and Mohamed Nadif},
  journal={Computational Statistics & Data Analysis},
The block or simultaneous clustering problem on a set of objects and a set of variables is embedded in the mixture model. Two algorithms have been developed: block EM as part of the maximum likelihood and fuzzy approaches, and block CEM as part of the classification maximum likelihood approach. A unified framework for obtaining different variants of block EM is proposed. These variants are studied and their performances evaluated in comparison with block CEM, two-way EM and two-way CEM, i.e EM… CONTINUE READING


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