  We introduce a new clustering method for the classification of functional
data sets by their probabilistic law, that is, a procedure that aims to assign
data sets to the same cluster if and only if the data were generated with the
same underlying distribution. This method has the nice virtue of being
non-supervised and non-parametric, allowing for exploratory investigation with
few assumptions about the data. Rigorous finite bounds on the classification
error are given along with an objective heuristic that consistently selects the
best partition in a data-driven manner. Simulated data has been clustered with
this procedure to show the performance of the method with different parametric
model classes of functional data.
