  Finite mixture models have become a popular tool for clustering. Amongst
other uses, they have been applied for clustering longitudinal data and
clustering high-dimensional data. In the latter case, a latent Gaussian mixture
model is sometimes used. Although there has been much work on clustering using
latent variables and on clustering longitudinal data, respectively, there has
been a paucity of work that combines these features. An approach is developed
for clustering longitudinal data with many time points based on an extension of
the mixture of common factor analyzers model. A variation of the
expectation-maximization algorithm is used for parameter estimation and the
Bayesian information criterion is used for model selection. The approach is
illustrated using real and simulated data.
