An iteratively reweighted approach for robust clustering is presented in
this work. The method is initialized with a very robust clustering partition
based on an high trimming level. The initial partition is then refined
to reduce the number of wrongly discarded observations and substantially
increase efficiency. Simulation studies and real data examples indicate that
the final clustering solution is both robust and efficient, and naturally adapts
to the true underlying contamination level.