Classification de données fonctionnelles par décomposition de mélange Apports de la visualisation dans le cas des distributions de probabilité

International audienceFunctional data can come from repeated measures, but also as result of statistical analysis. In symbolic data analysis a symbolic object can be described with a probability distribution. The clustering of such objects can be performed using a mixture decomposition with archimedean copulas on values of the distributions computed in q points, named intersection points. So far this points were chosen randomly. In this paper, using visualizations, we try, empirically, to understand what is the best choice for the number and the location of these intersections points. We propose also some rules to choose this parameter of the classification