Silhouette Analysis: A Guide to Choosing the Optimal Number of Clusters for k-means

Silhouette analysis studies the quality of clustering by measuring how similar an object is to its own cluster compared to other clusters. It calculates a silhouette coefficient for each object, which is a measure of how well the object fits into its assigned cluster. The silhouette coefficient ranges from -1 to 1, with higher values indicating better clustering. Silhouette analysis can be used to determine the optimal number of clusters for a given dataset.