Work out davies bouldin index instantly with clear inputs, formula shown and shareable results.
The Davies-Bouldin index measures, for each pair of clusters, how large their internal spread is relative to the distance between their centres: R = (Si + Sj) / Dij. The overall index is the mean of each cluster's worst-case pairing, and lower is better. A value near or above 1 means the clusters are as spread out as they are far apart, so they overlap.
Pairwise similarity
R(i,j) = (Si + Sj) / D(i,j); DB index = mean over i of max over j of R(i,j)
Lower is better within one dataset and one distance metric, but the index has no absolute scale, so use it to rank candidate clusterings rather than as a quality certificate.
Davies-Bouldin only uses centroids and average scatter, so it is cheap but biased toward convex, equally sized clusters. The silhouette uses all pairwise distances and handles irregular shapes better.