Self organising maps used for classification, illustration with several examples[Utilisation des cartes d’auto-organisation dans la classification, illustration par des exemples]

The aim of this paper is to illustrate, with several examples, the possible uses of self-organizing maps (som), especially in classification and dataset visualization purposes. This classification method, based on the unsupervised learning algorithm of Kohonen, is presented here in a practical manner, and applied in the following fields: characterisation of human skin, Polish national daily consumption of electricity, training supply and professional careers. The diversity of the examples, in these application fields and for various statistical purposes (typology, classification of curves), illustrate many self-organizing map characteristics as compared to more traditional methods. In particular, we should mention, firstly, that the clustering method and its representation system are complementary, secondly, that som are sensitive to small distances and, finally, that they can be pertinent when the data set is large. These properties make it possible to visualize cluster proximities, local effects (restricted to a part of the population) and integrate many variables into the analysis (for example, as in data mining). Apart from clustering, self-organizing maps can also be used as a visualization tool for the representation of data set intrinsic structure. In this case, as with projections to principal planes of the factorial analysis, this data set representation can be considered a graphical support for any analysis method. To illustrate its characteristics, we use it to represent two hierarchical classification results. In this instance, the particularity of this technique comes from the specificity of its own symbolic representation. It gives to som a freedom that allows it to be well adapted to complex structures such as non-linear ones. In this paper, self-organizing maps are presented first as a clustering method, and then as a tool for the visualization of dataset intrinsic structure.