  Co-clustering is a data mining technique used to extract the underlying block
structure between the rows and columns of a data matrix. Many approaches have
been studied and have shown their capacity to extract such structures in
continuous, binary or contingency tables. However, very little work has been
done to perform co-clustering on mixed type data. In this article, we extend
the latent block models based co-clustering to the case of mixed data
(continuous and binary variables). We then evaluate the effectiveness of the
proposed approach on simulated data and we discuss its advantages and potential
limits.
