Clustering Validation

Cluster validation involves evaluation of the clustering using external index by comparing the clustering results to ground truth (externally known results).

One approach to measuring cluster validity is to use external validation measures, such as the silhouette coefficient, to evaluate how well each point fits into its assigned cluster, or use the Rand index to compare the similarity between the clustering results and the ground truth.

It measures how distinct or well-separated a cluster is from other clusters. For example, the pairwise distances between cluster centers or the pairwise minimum distances between objects in different clusters are widely used as measures of separation.

Cluster validation is the process of evaluating the quality and performance of a clustering algorithm on a given data set. It can help you choose the optimal number of clusters, compare different clustering methods, and assess the stability and robustness of the clusters.