  Determining optimal number of clusters in a dataset is a challenging task.
Though some methods are available, there is no algorithm that produces unique
clustering solution. The paper proposes an Automatic Merging for Single Optimal
Solution (AMSOS) which aims to generate unique and nearly optimal clusters for
the given datasets automatically. The AMSOS is iteratively merges the closest
clusters automatically by validating with cluster validity measure to find
single and nearly optimal clusters for the given data set. Experiments on both
synthetic and real data have proved that the proposed algorithm finds single
and nearly optimal clustering structure in terms of number of clusters,
compactness and separation.
