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I'd like to evaluate my clustering results using only internal indices. For example Silhouette index (S) validates the clustering performance based on the pairwise difference of between- and within-cluster distances. I can't use this metrics on similarity matrix. Another example is Davies–Bouldin index (DB): For each cluster C, the similarities between C and all other clusters are computed, and the highest value is assigned to C as cluster similarity. And I think I can use this metrics. How I can uniforme my data for evaluate all the famous metrics? Like Silhouette, dunn index and so on.

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You can adapt the definition of Silhouette for similarity functions.

You can also transform your similarity into a dissimilarity; and since most internal measures do not require the triangle inequality, this should work just fine.

But: every auch "Index" ist just yet another heuristic, and a clustering algorithm (just one where we don't have a fast algorithm). So don't rely on them too much. You just get the same problem again: what is the best index?

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