  This paper proposes a simple, automatic and efficient clustering algorithm,
namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate
nearly optimal clusters for the given datasets automatically. The AMOC is an
extension to standard k-means with a two phase iterative procedure combining
certain validation techniques in order to find optimal clusters with automation
of merging of clusters. Experiments on both synthetic and real data have proved
that the proposed algorithm finds nearly optimal clustering structures in terms
of number of clusters, compactness and separation.

Computer Vision and Pattern Recognition