This method measures how similar a point is to its own cluster (cohesion) compared to other clusters (separation) for different values of k. The Silhouette Score reaches its global maximum at the optimal k.
The silhouette coefficient measures the quality of clustering by assessing how well each data point fits into its assigned cluster. It considers both the cohesion within the cluster and the separation between clusters. The optimal K can be chosen based on the highest average silhouette coefficient across different values of K.