Clustering Evaluation Without Labels

Three key metrics for evaluating clustering quality without labels are Silhouette score, Calinski-Harabasz Index, and DBCV (density-based clustering validation). The Silhouette score measures how well data points fit within their clusters compared to other clusters. The Calinski-Harabasz Index is similar but faster to compute. DBCV works better for density-based clustering, measuring density within clusters and overlap between them. These metrics help determine how well-separated and reliable clusters are, with higher scores generally indicating better clustering results.