Clustering data: dealing with high density variations

This paper focuses on the problem of cluster analysis when data present high variations of density. The proposed method is based upon a hierarchical clustering and enables one to determine the clusters without any assumption on their number nor their statistical distribution. This method is used to design an efficient distributed neural classifier which reveal a good generalization behavior on a real problem of handwriting digit recognition (NIST database)