  Co-clustering is a class of unsupervised data analysis techniques that
extract the existing underlying dependency structure between the instances and
variables of a data table as homogeneous blocks. Most of those techniques are
limited to variables of the same type. In this paper, we propose a mixed data
co-clustering method based on a two-step methodology. In the first step, all
the variables are binarized according to a number of bins chosen by the
analyst, by equal frequency discretization in the numerical case, or keeping
the most frequent values in the categorical case. The second step applies a
co-clustering to the instances and the binary variables, leading to groups of
instances and groups of variable parts. We apply this methodology on several
data sets and compare with the results of a Multiple Correspondence Analysis
applied to the same data.
