  A unified clustering approach that can estimate number of clusters and
produce clustering against this number simultaneously is proposed. Average
silhouette width (ASW) is a widely used standard cluster quality index. A
distance based objective function that optimizes ASW for clustering is defined.
The proposed algorithm named as OSil, only, needs data observations as an input
without any prior knowledge of the number of clusters. This work is about
thorough investigation of the proposed methodology, its usefulness and
limitations. A vast spectrum of clustering structures were generated, and
several well-known clustering methods including partitioning, hierarchical,
density based, and spatial methods were consider as the competitor of the
proposed methodology. Simulation reveals that OSil algorithm has shown superior
performance in terms of clustering quality than all clustering methods included
in the study. OSil can find well separated, compact clusters and have shown
better performance for the estimation of number of clusters as compared to
several methods. Apart from the proposal of the new methodology and it's
investigation the paper offers a systematic analysis on the estimation of
cluster indices, some of which never appeared together in comparative
simulation setup before. The study offers many insightful findings useful for
the selection of the clustering methods and indices for clustering quality
assessment.
