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Many data analysis methods cannot be applied to data that are not representedby a fixed number of real values, whereas most of real world observations arenot readily available in such a format. Vector based data analysis methods havetherefore to be adapted in order to be used with non standard complex data. Aflexible and general solution for this adaptation is to use a (dis)similaritymeasure. Indeed, thanks to expert knowledge on the studied data, it isgenerally possible to define a measure that can be used to make pairwisecomparison between observations. General data analysis methods are thenobtained by adapting existing methods to (dis)similarity matrices. In thisarticle, we propose an adaptation of Kohonen's Self Organizing Map (SOM) to(dis)similarity data. The proposed algorithm is an adapted version of thevector based batch SOM. The method is validated on real world data: we providean analysis of the usage patterns of the web site of the Institut National deRecherche en Informatique et Automatique, constructed thanks to web log miningmethod.

Une adaptation des cartes auto-organisatrices pour des donn\'ees d\'ecrites par un tableau de dissimilarit\'es