An upper bound on the silhouette evaluation metric for clustering
This paper derives for each data point a sharp upper bound on its silhouette width and aggregates these to obtain a canonical upper bound for the average silhouette width, thereby enhancing the interpretability of clustering quality evaluation by showing how close a given result is to the best possible outcome for that specific data.