Explore machine learning concepts related to K Selection. Clear explanations and practical insights.
How the Calinski-Harabasz index evaluates clustering quality by measuring the ratio of between-cluster to within-cluster variance — fast, intuitive, and ideal for k-selection with convex clusters.
How the silhouette score measures clustering quality for every individual point — comparing intra-cluster cohesion to nearest-cluster separation, with per-point diagnostics that work for arbitrary cluster shapes.
How the Davies-Bouldin index evaluates clustering quality by finding each cluster's most similar neighbor — a pessimistic, worst-case metric that catches overlapping cluster pairs.