Computer Science – Neural and Evolutionary Computing
Scientific paper
2007-09-22
Revue de Statistique Appliqu\'ee LIV, 3 (2006) 33-64
Computer Science
Neural and Evolutionary Computing
Scientific paper
Many data analysis methods cannot be applied to data that are not represented by a fixed number of real values, whereas most of real world observations are not readily available in such a format. Vector based data analysis methods have therefore to be adapted in order to be used with non standard complex data. A flexible and general solution for this adaptation is to use a (dis)similarity measure. Indeed, thanks to expert knowledge on the studied data, it is generally possible to define a measure that can be used to make pairwise comparison between observations. General data analysis methods are then obtained by adapting existing methods to (dis)similarity matrices. In this article, we propose an adaptation of Kohonen's Self Organizing Map (SOM) to (dis)similarity data. The proposed algorithm is an adapted version of the vector based batch SOM. The method is validated on real world data: we provide an analysis of the usage patterns of the web site of the Institut National de Recherche en Informatique et Automatique, constructed thanks to web log mining method.
Conan-Guez Brieuc
Golli Aïcha El
Lechevallier Yves
Rossi Fabrice
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