# A generalized Bayes framework for probabilistic clustering

@article{Rigon2020AGB, title={A generalized Bayes framework for probabilistic clustering}, author={Tommaso Rigon and Amy H. Herring and David B. Dunson}, journal={arXiv: Methodology}, year={2020} }

Loss-based clustering methods, such as k-means and its variants, are standard tools for finding groups in data. However, the lack of quantification of uncertainty in the estimated clusters is a disadvantage. Model-based clustering based on mixture models provides an alternative, but such methods face computational problems and large sensitivity to the choice of kernel. This article proposes a generalized Bayes framework that bridges between these two paradigms through the use of Gibbs…

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