  We propose an easy-to-use methodology to allocate one of the groups which
have been previously built from a complete learning data base, to new
individuals. The learning data base contains continuous and categorical
variables for each individual. The groups (clusters) are built by using only
the continuous variables and described with the help of the categorical ones.
For the new individuals, only the categorical variables are available, and it
is necessary to define a model which computes the probabilities to belong to
each of the clusters, by using only the categorical variables. Then this model
provides a decision rule to assign the new individuals and gives an efficient
tool to decision-makers. This tool is shown to be very efficient for customers
allocation in consumer clusters for marketing purposes, for example.
