I have a large data set and a cosine similarity between them. I would like to cluster them using cosine similarity that puts similar objects together without needing to specify beforehand the number of clusters I expect.
I read the sklearn documentation of DBSCAN and Affinity Propagation, where both of them requires a distance matrix (not cosine similarity matrix).
Really, I'm just looking for any algorithm that doesn't require a) a distance metric and b) a pre-specified number of clusters.
Does anyone know of an algorithm that would do that?