Probabilistic model-based clustering of multivariate and sequential data

Probabilistic model-based clustering, based on finite mixtures of multivariate models, is a useful framework for clustering data in a statistical context. This general framework can be directly extended to clustering of sequential data, based on finite mixtures of sequential models. In this paper we consider the problem of fitting mixture models where both multivariate and sequential observations are present. A general EM algorithm is discussed and experimental results demonstrated on simulated data. The problem is motivated by the practical problem of clustering individuals into groups based on both their static characteristics and their dynamic behavior.