Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data
Generative approaches to clustering provide information on geometric properties of clusters, whereas discriminative approaches provide boundaries between clusters. Ideas from both approaches are incorporated to present a fully unsupervised, probabilistic, and discriminative clustering method via a regularized mutual information objective function, wherein a mixture of mixtures of Gaussian and uniform distributions is used for formulation of the conditional model. Overfitting is avoided by the introduction of a regularizing term and a cluster merge step, similar to those applied in reversible jump Markov chain Monte Carlo methods used in Bayesian clustering. Consequently, the turtle shell ...
| Authors | Mackenzie R. Neal, Paul D. McNicholas, Arthur White |
|---|---|
| Primary category | stat.ML |
| Published | 25 April 2026 |
| Last updated at source | 20 July 2026 |
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