Silhouette Score

INFO

Measures how similar a data point is to its own cluster compared to other clusters. A higher score indicates better-defined clusters.

How It Works

For a data point $i$:

$$s(i)=\frac{b(i)-a(i)}{\max (a(i),b(i))}$$

• $a(i)$: Average distance from $i$ to other points in the same cluster
• $b(i)$: Minimum average distance from $i$ to points in a different cluster

What to Look For

• Range: $-1$ to $+1$
• Close to ($+1$): Well-clustered
• Close to ($0$): On the boundary
• Close to ($-1$): Possibly misclassified

Application Models

• K-Means Clustering
• Hierarchical Clustering
• Density-Based Spatial Clustering of Application with Noise (DBSCAN)
• Gaussian Mixture Models