The Limitation Of Silhouette Score Which Is Often Ignored By Many

Silhouette score is commonly used for evaluating clustering results.

At times, it is also preferred in place of the elbow curve to determine the optimal number of clusters. (I have covered this before if you wish to recap or learn more).

However, while using the Silhouette score, it is also important to be aware of one of its major shortcomings.

The Silhouette score is typically higher for convex (or somewhat spherical) clusters.

However, using it to evaluate arbitrary-shaped clustering can produce misleading results.

This is also evident from the following image:

While the clustering output of KMeans is worse, the Silhouette score is still higher than Density-based clustering.

DBCV — density-based clustering validation is a better metric in such cases.

As the name suggests, it is specifically meant to evaluate density-based clustering.

Simply put, DBCV computes two values:

• The density within a cluster

• The density between clusters

A high density within a cluster and a low density between clusters indicates good clustering results.

DBCV can also be used when you don’t have ground truth labels.

This adds another metric to my recently proposed methods: Evaluate Clustering Performance Without Ground Truth Labels.

The effectiveness of DBCV is also evident from the image below:

This time, the score for the clustering output of KMeans is worse, and that of density-based clustering is higher.

Get started with DBCV here: GitHub.

👉 Over to you: What are some other ways to evaluate clustering where traditional metrics may not work?

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.