I am clustering time-series datasets which are not labeled (No Ground truth) and I want to measure the quality of the clusters. Could you please suggest any Clustering performance evaluation methods that can be used in time-series clustering ??


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Clustering evaluation metrics basic goal is to measure the similarity within each cluster and dissimilarity among clusters; if a clustering algorithm has achieved those two things in an acceptable degree than it has performed well.

The most commonly used evaluation metrics are:

-Silhouette Coefficient - it is the most popular one for time-series clustering (implementation for python can be found in tslearn.clustering package)

-Davies-Bouldin Index (implemenation for python can be found in sklearn package)

-Dunn's index

You might find the following articles useful sources to help you understand those metrics:

Finally, a more complete collection of clustering evaluation measures can be found in this review.


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