# A score function for Bayesian cluster analysis

## TL;DR

This paper introduces a parameter-free score function for Bayesian clustering that balances within-cluster variance and between-cluster entropy, aiding in selecting the optimal number of clusters.

## Contribution

The proposed score function is a novel, parameter-free tool for Bayesian clustering that improves cluster number selection in existing methods.

## Key findings

- Effective in choosing the number of clusters
- Balances variance and entropy considerations
- Applicable to hierarchical and K-means clustering

## 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.