  This paper deals with a clustering algorithm for histogram data based on a
Self-Organizing Map (SOM) learning. It combines a dimension reduction by SOM
and the clustering of the data in a reduced space. Related to the kind of data,
a suitable dissimilarity measure between distributions is introduced: the $L_2$
Wasserstein distance. Moreover, the number of clusters is not fixed in advance
but it is automatically found according to a local data density estimation in
the original space. Applications on synthetic and real data sets corroborate
the proposed strategy.
