A general trimming approach to robust cluster analysis

We introduce a new method for performing clustering with the aim of ¯tting clusters with di®erent scatters and weights. It is designed by allowing to handle a proportion ® 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-de¯ned 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. The proposal includes an algorithm (the TCLUST method) for approximately solving the sample problem.