Silhouette analysis measures how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette value ranges from -1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. This value can be computed for each data point, and averages can be determined for clusters or the entire dataset.
