Silhouette Score Helper
Paste 2D or 3D points with cluster labels, get per-point silhouette scores and the overall mean to evaluate your clustering.
Data Points
One point per line: x,y,label or x,y,z,label
Results
Enter data points and click Compute
Mean Silhouette Score
-1 (poor)
0
+1 (great)
| # | Point | Cluster | a(i) | b(i) | s(i) |
|---|
Score Reference
0.71 – 1.00
Strong structure — clusters are tight and well-separated.
0.26 – 0.70
Reasonable structure — some overlap or density variation.
Below 0.26
Weak or no structure — consider different k or algorithm.
Summary
Paste 2D or 3D points with cluster labels, get per-point silhouette scores and the overall mean to evaluate your clustering.
How it works
- Enter one data point per line in the format: x,y,label or x,y,z,label.
- Click "Compute" to calculate pairwise Euclidean distances between all points.
- For each point i, a(i) = mean distance to all other points in the same cluster.
- For each point i, b(i) = mean distance to all points in the nearest other cluster.
- Silhouette score s(i) = (b(i) - a(i)) / max(a(i), b(i)), ranging from -1 to +1.
Use cases
- Evaluate whether k-means or DBSCAN produced tight, well-separated clusters.
- Compare cluster counts by checking which k gives the highest mean silhouette score.
- Identify misclassified points with negative silhouette scores for re-labeling.
- Validate clustering results from a CSV export before reporting to stakeholders.
Frequently Asked Questions
Last updated: 2026-07-22 ·
Reviewed by Nham Vu