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What’s the best method to evaluate clustering without labels?

Asked on Nov 08, 2025

Answer

Evaluating clustering without labels typically involves using internal validation metrics that assess the quality of the clusters based on the data's inherent structure. One of the most common methods is the Silhouette Score, which measures how similar an object is to its own cluster compared to other clusters.

Example Concept: The Silhouette Score is calculated for each sample, measuring the mean distance between a sample and all other points in the same cluster (a) and the mean distance between a sample and all points in the nearest cluster (b). The Silhouette Score for a sample is (b - a) / max(a, b), with values ranging from -1 to 1. A higher score indicates better-defined clusters, with 1 being the best, 0 indicating overlapping clusters, and negative values suggesting incorrect clustering.

Additional Comment:

Other methods include the Davies-Bouldin Index and the Dunn Index, which also assess cluster compactness and separation.
Visual methods like the Elbow Method can help determine the optimal number of clusters by plotting the within-cluster sum of squares.
It's important to consider the context and domain-specific requirements when choosing a clustering evaluation metric.

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What’s the best method to evaluate clustering without labels?

Asked on Nov 08, 2025

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