SOM-based algorithms for qualitative variables

Details SOM-based algorithms for qualitative variables SOM-based algorithms for qualitative variables

2006-10-19

arxiv.org/abs/math/0610584v1

Neural networks 17 (10/2004) 1149-1167

Mathematics

Statistics Theory

Special Issue apr\`{e}s WSOM 03 \`{a} Kitakiushu

Scientific paper

10.1016/j.neunet.2004.07.010

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.

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