# Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data

arXiv: https://arxiv.org/abs/2604.23083 · HTML: https://mlsanity.com/paper/2604.23083/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 25 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Generalised Mutual Information for Discriminative Clustering](https://mlsanity.com/paper/2210.06300/) (2022)
- [Kernel clustering: density biases and solutions](https://mlsanity.com/paper/1705.05950/) (2017)
- [Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data](https://mlsanity.com/paper/1702.04407/) (2017)

## Abstract (arXiv)

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 method -- a fully unsupervised clustering method capable of estimating non-linear boundary lines, automatically selecting the number of components, and capturing intuitive clusters in the presence of data abnormalities such as noise and/or irregular cluster shapes -- is introduced. We test this method on various simulated and real datasets commonly explored in clustering research, and extend the analysis to datasets arising from flow cytometry experiments and image analysis.