I have two ways to cluster a set of objects, and now I want to compare my two clusters so to measure how "similar" the resulting clusters are.
I found there is a variety of validation criteria (Hubert's gamma coefficient, the Dunn index and the corrected rand index), but the implementation I found for them rely on knowing a distance matrix for pairs of objects (which I don't have).
(As can be seen here - see last paragraph)
My question is if other measures exist? (such that don't require the knowledge of the distance matrix)
(I already found one such solution, using a measure called Bk, from C mallow in 1983 - but would like to know of other solutions that came to be since then)