On an adjacency cluster merit approach

This work addresses the cluster validation problem of determining the 'right' number of clusters. We consider a cluster stability property based on the k‐nearest neighbour type coincidences model. Quality of a clustering is measured by the deviation from this model, where a small deviation indicates a good clustering. The true number of clusters corresponds to the empirical deviation distribution having the shortest right tail. Experiments carried out on synthetic and real data sets demonstrate the effectiveness of our method.