SOM-based algorithms for qualitative variables

Authors:
Marie Cottrell;Smail Ibbou;Patrick Letrémy
Affiliations:
SAMOS-MATISSE UMR 8595, 90 rue de Tolbiac, F-75634 Paris Cedex 13, France;SAMOS-MATISSE UMR 8595, 90 rue de Tolbiac, F-75634 Paris Cedex 13, France;SAMOS-MATISSE UMR 8595, 90 rue de Tolbiac, F-75634 Paris Cedex 13, France
Venue:
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Year:
2004

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Abstract

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.