# Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data

arXiv: https://arxiv.org/abs/2202.13312 · HTML: https://mlsanity.com/paper/2202.13312/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 66 references that are in the map.
- First posted on arXiv 27 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Data Stream Clustering: A Review](https://mlsanity.com/paper/2007.10781/) (2020)
- [Streaming, Distributed Variational Inference for Bayesian Nonparametrics](https://mlsanity.com/paper/1510.09161/) (2015)
- [Blackbox: A procedure for parallel optimization of expensive black-box functions](https://mlsanity.com/paper/1605.00998/) (2016)
- [Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture](https://mlsanity.com/paper/1305.6659/) (2013)
- [Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models](https://mlsanity.com/paper/1707.08493/) (2017)

## Built on by (most cited first, 6 of 6)

- [DeepDPM: Deep Clustering With an Unknown Number of Clusters](https://mlsanity.com/paper/2203.14309/) (2022)
- [Streaming Inference for Infinite Non-Stationary Clustering](https://mlsanity.com/paper/2205.01212/) (2022)
- [Learning non-stationary and discontinuous functions using clustering, classification and Gaussian process modelling](https://mlsanity.com/paper/2211.16909/) (2022)
- [Online Clustering of Seafloor Imagery for Interpretation during Long-Term AUV Operations](https://mlsanity.com/paper/2509.06678/) (2025)
- [Dirichlet process mixture models for non-stationary data streams](https://mlsanity.com/paper/2210.06872/) (2022)
- [Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning](https://mlsanity.com/paper/2409.03938/) (2024)

## Abstract (arXiv)

Practical tools for clustering streaming data must be fast enough to handle the arrival rate of the observations. Typically, they also must adapt on the fly to possible lack of stationarity; i.e., the data statistics may be time-dependent due to various forms of drifts, changes in the number of clusters, etc. The Dirichlet Process Mixture Model (DPMM), whose Bayesian nonparametric nature allows it to adapt its complexity to the data, seems a natural choice for the streaming-data case. In its classical formulation, however, the DPMM cannot capture common types of drifts in the data statistics. Moreover, and regardless of that limitation, existing methods for online DPMM inference are too slow to handle rapid data streams. In this work we propose adapting both the DPMM and a known DPMM sampling-based non-streaming inference method for streaming-data clustering. We demonstrate the utility of the proposed method on several challenging settings, where it obtains state-of-the-art results while being on par with other methods in terms of speed.