Clustering is a canonical example of un-supervised machine learning methods. Un-supervised, as in, true clusters (segments) don’t exist or aren’t known in advance. Hence method tries to separate observations in different groups without any way to verify if model has done good job or not. There are various ways we can try to measure performance of un-supervised clustering: Within-Cluster-Sum-of-Squares is one, Silhouette Coefficient is another