  Background: Gaussian mixture modeling is a fundamental tool in clustering, as
well as discriminant analysis and semiparametric density estimation. However,
estimating the optimal model for any given number of components is an NP-hard
problem, and estimating the number of components is in some respects an even
harder problem. Findings: In R, a popular package called mclust addresses both
of these problems. However, Python has lacked such a package. We therefore
introduce AutoGMM, a Python algorithm for automatic Gaussian mixture modeling,
and its hierarchical version, HGMM. AutoGMM builds upon scikit-learn's
AgglomerativeClustering and GaussianMixture classes, with certain modifications
to make the results more stable. Empirically, on several different
applications, AutoGMM performs approximately as well as mclust, and sometimes
better. Conclusions: AutoMM, a freely available Python package, enables
efficient Gaussian mixture modeling by automatically selecting the
initialization, number of clusters and covariance constraints.
