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We introduce a new statistical model for clustering populations of functions and multidimensional curves where the domain is a real interval  I . We consider that we are given a finite set of observations from a population of curves or functions with values infor a fixedand arguments in  I . We are interested in the population background where the clustering is the process of grouping curves into homogeneous sub-populations. In particular, we define a distribution function for each sub-population and use the statistical geometry of the the space of smooth densities to explore a Bayesian model with a spherical Gaussian process prior. We also give the expression of the log-posterior distribution on coefficients resulting from the expansion. Finally, the practical interest of the proposed method is illustrated on simulated and real datasets of multidimensional curves.

Bayesian cluster analysis for registration and clustering homogeneous subgroups in multidimensional functional data