  Recent advances in engineering technologies have enabled the collection of a
large number of longitudinal features. This wealth of information presents
unique opportunities for researchers to investigate the complex nature of
diseases and uncover underlying disease mechanisms. However, analyzing such
kind of data can be difficult due to its high dimensionality, heterogeneity and
computational challenges. In this paper, we propose a Bayesian nonparametric
mixture model for clustering high-dimensional mixed-type (e.g., continuous,
discrete and categorical) longitudinal features. We employ a sparse factor
model on the joint distribution of random effects and the key idea is to induce
clustering at the latent factor level instead of the original data to escape
the curse of dimensionality. The number of clusters is estimated through a
Dirichlet process prior. An efficient Gibbs sampler is developed to estimate
the posterior distribution of the model parameters. Analysis of real and
simulated data is presented and discussed. Our study demonstrates that the
proposed model serves as a useful analytical tool for clustering
high-dimensional longitudinal data.
