# AutoGMM: Automatic Gaussian Mixture Modeling in Python

arXiv: https://arxiv.org/abs/1909.02688 · HTML: https://mlsanity.com/paper/1909.02688/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables](https://mlsanity.com/paper/2304.03853/) (2023)

## Abstract (arXiv)

The exponential growth of complex data demands fully automatic clustering. Gaussian mixture models (GMMs) provide uncertainty-aware grouping but often require expertise to specify hyperparameters, e.g., component count and covariance structure. While mclust (R) automates this via Bayesian Information Criterion (BIC), Python lacks a comparable tool. We introduce AutoGMM, an open-source Python package automating GMM via strategic initialization using an agglomerative Mahalanobis heuristic, and parallelized model selection by information criteria. AutoGMM is a drop-in tool that yields strong out-of-the-box performance on classic benchmarks, targeted stress tests, and two real datasets, with favorable runtime scaling. The code is available at https://github.com/neurodata/AutoGMM with tests and reproducible workflows.