  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.
