  We propose a MAP Bayesian approach to perform and evaluate a co-clustering of
mixed-type data tables. The proposed model infers an optimal segmentation of
all variables then performs a co-clustering by minimizing a Bayesian model
selection cost function. One advantage of this approach is that it is user
parameter-free. Another main advantage is the proposed criterion which gives an
exact measure of the model quality, measured by probability of fitting it to
the data. Continuous optimization of this criterion ensures finding better and
better models while avoiding data over-fitting. The experiments conducted on
real data show the interest of this co-clustering approach in exploratory data
analysis of large data sets.
