  We propose a score function for Bayesian clustering. The function is
parameter free and captures the interplay between the within cluster variance
and the between cluster entropy of a clustering. It can be used to choose the
number of clusters in well-established clustering methods such as hierarchical
clustering or $K$-means algorithm.
