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Clustering Quality Score Calculator

Evaluate clustering quality using silhouette score, Calinski-Harabasz index, Davies-Bouldin index, and Dunn index from cluster distance metrics.

Inputs

Results

Silhouette Score

0.5

Quality Assessment

Weak structure

Calinski-Harabasz Index594
Davies-Bouldin Index0.5
Dunn Index2
Separation Ratio2
Avg Points per Cluster100
How to Use This Calculator
  1. Enter the number of clusters and within-cluster sum of squares (WCSS) values.
  2. Set the between-cluster sum of squares for separation measurement.
  3. Review the silhouette score and Calinski-Harabasz index.
  4. Compare metrics across different values of k to identify the optimal cluster count.
  5. Use the elbow method on WCSS and peak silhouette score to finalize cluster selection.

What each input means

Avg Intra-Cluster Distance
Average distance between points within the same cluster. Lower is better (tighter clusters).
Avg Nearest-Cluster Distance
Average distance from points to the nearest neighboring cluster. Higher is better (more separated).
Number of Clusters (k)
The number of clusters in your clustering solution.
Total Data Points
Total number of data points across all clusters.

How this is calculated

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    Avg Intra-Cluster Distance = 2.5, Avg Nearest-Cluster Distance = 5, Number of Clusters (k) = 3, Total Data Points = 300 = 4 input(s) provided
  2. Calculate Silhouette Score
    Silhouette Score
    0.5 = 0.5
  3. Calculate Quality Assessment
    Weak structure = Weak structure
  4. Calculate Calinski-Harabasz Index
    Calinski-Harabasz Index
    594 = 594
  5. Calculate Davies-Bouldin Index
    Davies-Bouldin Index
    0.5 = 0.5

Engine last updated . Checked against 1 independently-derived test — how we verify calculators.

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