# Bayesian cluster analysis: Point estimation and credible balls

arXiv: https://arxiv.org/abs/1505.03339 · HTML: https://mlsanity.com/paper/1505.03339/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2015.

## Built on by (most cited first, 16 of 16)

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- [Dirichlet process mixture model based on topologically augmented signal representation for clustering infant vocalizations](https://mlsanity.com/paper/2407.05760/) (2024)
- [Scalable Posterior Uncertainty for Flexible Density-Based Clustering](https://mlsanity.com/paper/2603.03188/) (2026)
- [Vertical Consensus Inference for High-Dimensional Random Partition](https://mlsanity.com/paper/2603.27864/) (2026)
- [Posterior Regularization on Bayesian Hierarchical Mixture Clustering](https://mlsanity.com/paper/2105.06903/) (2021)
- [Entropy regularization in probabilistic clustering](https://mlsanity.com/paper/2307.10065/) (2023)
- [The generalized underlap coefficient with an application in clustering](https://mlsanity.com/paper/2602.19473/) (2026)
- [Explainable cluster analysis: a bagging approach](https://mlsanity.com/paper/2603.19840/) (2026)
- [Cluster-Aware Matching via Laplacian Optimal Transport](https://mlsanity.com/paper/2607.16178/) (2026)
- [Bracketing Uncertainty in Clustering Under the Manifold Hypothesis](https://mlsanity.com/paper/2609.17892/) (2026)

## Abstract (arXiv)

Clustering is widely studied in statistics and machine learning, with applications in a variety of fields. As opposed to classical algorithms which return a single clustering solution, Bayesian nonparametric models provide a posterior over the entire space of partitions, allowing one to assess statistical properties, such as uncertainty on the number of clusters. However, an important problem is how to summarize the posterior; the huge dimension of partition space and difficulties in visualizing it add to this problem. In a Bayesian analysis, the posterior of a real-valued parameter of interest is often summarized by reporting a point estimate such as the posterior mean along with 95% credible intervals to characterize uncertainty. In this paper, we extend these ideas to develop appropriate point estimates and credible sets to summarize the posterior of clustering structure based on decision and information theoretic techniques.