I am trying to cluster textual data using fastText vectors with different clustering algorithms, mainly K-Means and DBSCAN.
I would like to know which internal evaluation metric works best with K-means and DBSCAN (ex: silhouette coefficient).
More specifically, I am looking for a metric that does not give higher values to convex-shaped like the silhouette coefficient in order to be able to compare clustering obtained with K-Means and DBSCAN.
My understanding is that internal clustering metrics are not only to be used for comparing a different number of clusters or clustering methods. What decision should I make if different metrics contradict each other i.e if one metric indicates an improvement and another, a decrease in clustering quality?