The Silhouette Method measures how similar an object is to its own cluster compared to other clusters. The silhouette score 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. If most objects have a high value, the clustering configuration is appropriate. **Implementation Steps:** - For various values of k, perform k-means clustering and for each, compute the average silhouette score. - Choose the value of k that maximizes the average silhouette score.