Section: New Results

Model-based clustering for functional data

Participants : Julien Jacques, Cristian Preda.

Two procedures for clustering functional data have been developed.

The first one, published in [14] , is based on a functional latent mixture model which fits the functional data in group-specific functional subspaces. By constraining model parameters within and between groups, a family of parsimonious models is exhibited which allows to fit onto various situations. An estimation procedure based on the EM algorithm is proposed for estimating both the model parameters and the group-specific functional subspaces. Experiments on real-world datasets show that the proposed approach performs better or similarly than classical clustering methods while providing useful interpretations of the groups.

The second procedure, currently submitted, is a model-based clustering procedure, defined on the basis of an approximation of the density of functional random variables [36] . As previously, the EM algorithm is used for parameter estimation and the maximum a posteriori rule provides the clusters. Simulation study and real data application illustrate the interest of this methodology.