# Model Based Clustering for Mixed Data: clustMD

arXiv: https://arxiv.org/abs/1511.01720 · HTML: https://mlsanity.com/paper/1511.01720/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2015.

## Built on by (most cited first, 8 of 8)

- [Model-based Clustering with Missing Not At Random Data](https://mlsanity.com/paper/2112.10425/) (2021)
- [Spectral Clustering of Categorical and Mixed-type Data via Extra Graph Nodes](https://mlsanity.com/paper/2403.05669/) (2024)
- [Mixed Data Clustering Survey and Challenges](https://mlsanity.com/paper/2512.03070/) (2025)
- [Model Based Co-clustering of Mixed Numerical and Binary Data](https://mlsanity.com/paper/2212.11725/) (2022)
- [MMM: Clustering Multivariate Longitudinal Mixed-type Data](https://mlsanity.com/paper/2509.12166/) (2025)
- [Clustering Approaches for Mixed-Type Data: A Comparative Study](https://mlsanity.com/paper/2511.19755/) (2025)
- [PretopoMD: Pretopology-based Mixed Data Hierarchical Clustering](https://mlsanity.com/paper/2512.03071/) (2025)
- [Moment-Based Anomaly Detection in High-Dimensional Mixed Data](https://mlsanity.com/paper/2610.16285/) (2026)

## Abstract (arXiv)

A model based clustering procedure for data of mixed type, clustMD, is developed using a latent variable model. It is proposed that a latent variable, following a mixture of Gaussian distributions, generates the observed data of mixed type. The observed data may be any combination of continuous, binary, ordinal or nominal variables. clustMD employs a parsimonious covariance structure for the latent variables, leading to a suite of six clustering models that vary in complexity and provide an elegant and unified approach to clustering mixed data. An expectation maximisation (EM) algorithm is used to estimate clustMD; in the presence of nominal data a Monte Carlo EM algorithm is required. The clustMD model is illustrated by clustering simulated mixed type data and prostate cancer patients, on whom mixed data have been recorded.