  We propose a clustering procedure to group K populations into subgroups with
the same dependence structure. The method is adapted to paired population and
can be used with panel data. It relies on the differences between orthogonal
projection coefficients of the K density copulas estimated from the K
populations. Each cluster is then constituted by populations having
significantly similar dependence structures. A recent test statistic from
Ngounou-Bakam and Pommeret (2022) is used to construct automatically such
clusters. The procedure is data driven and depends on the asymptotic level of
the test. We illustrate our clustering algorithm via numerical studies and
through two real datasets: a panel of financial datasets and insurance dataset
of losses and allocated loss adjustment expense.
