Model Based Clustering for Mixed Data: clustMD

TL;DR

clustMD introduces a unified model-based clustering method for mixed data types using a latent Gaussian mixture model, employing EM algorithms for estimation, demonstrated on simulated and real prostate cancer data.

Contribution

It develops a novel latent variable model that unifies clustering of mixed data types with a parsimonious covariance structure and an EM estimation approach.

Findings

Effective clustering of simulated mixed data.

Successful application to prostate cancer patient data.

Provides a flexible framework for mixed data clustering.

Abstract

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.