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A score function for Bayesian cluster analysis (1905.10209v1)

Published 24 May 2019 in stat.OT

Abstract: 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.

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