  We introduce a new method for performing clustering with the aim of fitting
clusters with different scatters and weights. It is designed by allowing to
handle a proportion $\alpha$ of contaminating data to guarantee the robustness
of the method. As a characteristic feature, restrictions on the ratio between
the maximum and the minimum eigenvalues of the groups scatter matrices are
introduced. This makes the problem to be well defined and guarantees the
consistency of the sample solutions to the population ones. The method covers a
wide range of clustering approaches depending on the strength of the chosen
restrictions. Our proposal includes an algorithm for approximately solving the
sample problem.
