Cartes auto-organis\'{e}es pour l'analyse exploratoire de donn\'{e}es et la visualisation
Statistics Theory
2016-08-16 v1 Neural and Evolutionary Computing
Adaptation and Self-Organizing Systems
Statistics Theory
Abstract
This paper shows how to use the Kohonen algorithm to represent multidimensional data, by exploiting the self-organizing property. It is possible to get such maps as well for quantitative variables as for qualitative ones, or for a mixing of both. The contents of the paper come from various works by SAMOS-MATISSE members, in particular by E. de Bodt, B. Girard, P. Letr\'{e}my, S. Ibbou, P. Rousset. Most of the examples have been studied with the computation routines written by Patrick Letr\'{e}my, with the language IML-SAS, which are available on the WEB page http://samos.univ-paris1.fr.
Keywords
Cite
@article{arxiv.math/0611422,
title = {Cartes auto-organis\'{e}es pour l'analyse exploratoire de donn\'{e}es et la visualisation},
author = {Marie Cottrell and SmaÏl Ibbou and Patrick Letrémy and Patrick Rousset},
journal= {arXiv preprint arXiv:math/0611422},
year = {2016}
}
Comments
Article de synth\`{e}se sur les applications de l'algorithme de Kohonen pour la visualisation et l'analyse de donn\'{e}es