Hi.
I'm dealing with clustering of data were the resulting clusters are, in general, non-spherical. Some of them are not convex.
What are the best internal metrics for evaluating these kind of clusters?
I know the silhouette index is a very common for evaluating the result of clustering process. However, it seems that silhouette index is biased towards spherical clusters.
Have a look at ::: https://repository.lib.fit.edu/bitstream/handle/11141/125/cs-2003-18.pdf?sequence=1
https://arxiv.org/pdf/1605.00316.pdf
https://repository.lib.fit.edu/bitstream/handle/11141/125/cs-2003-18.pdf?sequence=1
Hello,
I think the meaning of "good clustering" is subjective and its interpretation varies with applications. The notion of "good clustering" is relative, and it is a question of point of view.
However, there are few well known measures like silhouette width (SW), the Davies-Bouldin index (DB), the Calinski-Harabasz index (CH), the Normalized Mutual Information(NMI) and the Dunn index.
In my point of view, I think that the single-link metric is flexible in the sense that it can find non-isotropic clusters and also the clusters can be even concentric. The single-link metric works best for well-separated, non-spherical clusters.
Good luck.
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