  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.

Methodology