How do I interpret silhouette scores in clustering?

What are silhouette scores and how do I interpret them?

Silhouette scores measure how similar an object is to its own cluster compared to other clusters. A score close to +1 indicates that the object is well matched to its own cluster, while a score near -1 suggests it may be in the wrong cluster.

What is a good silhouette score?

A good silhouette score is generally above 0.5, indicating that the clusters are well-defined. Scores closer to 1 are even better.

Can you explain how to calculate silhouette scores?

Sure! The silhouette score for a single sample is calculated as (b - a) / max(a, b), where 'a' is the average distance to other points in the same cluster, and 'b' is the average distance to points in the nearest cluster.

What should I do if my silhouette score is low?

If your silhouette score is low, consider trying different clustering algorithms, adjusting the number of clusters, or examining your data for noise or outliers.