# Flexible Models for Microclustering with Application to Entity Resolution

arXiv: https://arxiv.org/abs/1610.09780 · HTML: https://mlsanity.com/paper/1610.09780/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2016.

## Built on by (most cited first, 9 of 9)

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- [Why the Rich Get Richer? On the Balancedness of Random Partition Models](https://mlsanity.com/paper/2201.12697/) (2022)
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- [A Primer on the Data Cleaning Pipeline](https://mlsanity.com/paper/2307.13219/) (2023)
- [Entropy regularization in probabilistic clustering](https://mlsanity.com/paper/2307.10065/) (2023)
- [Rapidly Varying Completely Random Measures for Modeling Extremely Sparse Networks](https://mlsanity.com/paper/2505.13206/) (2025)
- [Large-scale entity resolution via microclustering Ewens--Pitman random partitions](https://mlsanity.com/paper/2507.18101/) (2025)
- [Khatri-Rao Clustering for Data Summarization](https://mlsanity.com/paper/2603.06602/) (2026)
- [Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution](https://mlsanity.com/paper/2605.25814/) (2026)

## Abstract (arXiv)

Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pitman--Yor process mixture models make this assumption, as do all other infinitely exchangeable clustering models. However, for some applications, this assumption is inappropriate. For example, when performing entity resolution, the size of each cluster should be unrelated to the size of the data set, and each cluster should contain a negligible fraction of the total number of data points. These applications require models that yield clusters whose sizes grow sublinearly with the size of the data set. We address this requirement by defining the microclustering property and introducing a new class of models that can exhibit this property. We compare models within this class to two commonly used clustering models using four entity-resolution data sets.