  It is well known that the SOM algorithm achieves a clustering of data which
can be interpreted as an extension of Principal Component Analysis, because of
its topology-preserving property. But the SOM algorithm can only process
real-valued data. In previous papers, we have proposed several methods based on
the SOM algorithm to analyze categorical data, which is the case in survey
data. In this paper, we present these methods in a unified manner. The first
one (Kohonen Multiple Correspondence Analysis, KMCA) deals only with the
modalities, while the two others (Kohonen Multiple Correspondence Analysis with
individuals, KMCA\_ind, Kohonen algorithm on DISJonctive table, KDISJ) can take
into account the individuals, and the modalities simultaneously.
