This work is addressed to the problem of cluster validation to determine the right number of clusters. We consider a cluster stability property based on the k nearest neighbor type coincidences model. Cluster quality is measured by the deviations from this model such that good constructed clusters are typified by small departures values. The true number of clusters corresponds to the empirical deviation distribution having shortest right tail. The experiments carried out on synthetic and real databases demonstrate the effectiveness of the approach.