# Reducing over-clustering via the powered Chinese restaurant process

arXiv: https://arxiv.org/abs/1802.05392 · HTML: https://mlsanity.com/paper/1802.05392/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [A survey on Bayesian inference for Gaussian mixture model](https://mlsanity.com/paper/2108.11753/) (2021)
- [Why the Rich Get Richer? On the Balancedness of Random Partition Models](https://mlsanity.com/paper/2201.12697/) (2022)
- [A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws](https://mlsanity.com/paper/2604.24037/) (2026)

## Abstract (arXiv)

Dirichlet process mixture (DPM) models tend to produce many small clusters regardless of whether they are needed to accurately characterize the data - this is particularly true for large data sets. However, interpretability, parsimony, data storage and communication costs all are hampered by having overly many clusters. We propose a powered Chinese restaurant process to limit this kind of problem and penalize over clustering. The method is illustrated using some simulation examples and data with large and small sample size including MNIST and the Old Faithful Geyser data.