Multiple model-based clustering is achieved by splitting the variables into blocks. Each block of variables is modelled by a mixture model for achieving the clustering purpose. Model selection (block repartition, number of components and number of blocks) is managed with information criteria (penalized likelihood or MICL). Parameter inference is done by maximum likelihood.
|Author||Marbac, M. and Vandewalle, V.|
|Maintainer||Matthieu Marbac <email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on R-Forge|
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