Section: New Results

Clustering for functional data into discriminative subspaces

Participant : Julien Jacques.

This is a joint work with Charles Bouveyron (Paris 5) and Etienne Côme (Inrets).

A model-based clustering method for time series has been developed, based on a discriminative functional mixture model which allows the clustering of the data in a functional subspace. This model presents the advantage to be parsimonious and can therefore handle long time series. This model has been used for analyzing different bike sharing systems In Europe.