What are the differences between Silhouette Score and Davies-Bouldin Index?

What are the differences between Silhouette Score and Davies-Bouldin Index?

The Silhouette Score measures how similar an object is to its own cluster compared to other clusters, while the Davies-Bouldin Index evaluates the average similarity ratio of each cluster with its most similar cluster.

Which one is better for evaluating clustering performance?

It depends on the context. The Silhouette Score is more intuitive and ranges from -1 to 1, while the Davies-Bouldin Index should be minimized, with lower values indicating better clustering.

Can you give an example of how to calculate both?

Sure! For the Silhouette Score, you calculate the average distance between points in the same cluster and the nearest cluster. For the Davies-Bouldin Index, you compute the ratio of the sum of the within-cluster scatter to the between-cluster separation.

Are there any limitations to these metrics?

Yes, both metrics can be sensitive to noise and outliers. The Silhouette Score may not perform well with clusters of varying densities, while the Davies-Bouldin Index can be affected by the number of clusters.