SOM-based algorithms for qualitative variables

Marie Cottrell (1, 2) , Smail Ibbou (1, 2) , Patrick Letrémy (1, 2)

1 SAMOS - Statistique Appliquée et MOdélisation Stochastique
2 MATISSE - UMR 8595 - Modélisation Appliquée, Trajectoires Institutionnelles et Stratégies Socio-Économiques

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.

Mots clés: Data Analysis; Categorical Data; Kohonen Maps; Data Visualization; Data Mining

Domaines: Statistiques [math.ST]; Informatique

Marie Cottrell, Smail Ibbou, Patrick Letrémy. SOM-based algorithms for qualitative variables. Neural Networks, 2004, 17, pp.1149-1167. ⟨10.1016/j.neunet.2004.07.010⟩. ⟨hal-00107960⟩