  We propose a new class of robust and Fisher-consistent estimators for mixture
models. These estimators can be used to construct robust model-based clustering
procedures. We study in detail the case of multivariate normal mixtures and
propose a procedure that uses S estimators of multivariate location and
scatter. We develop an algorithm to compute the estimators and to build the
clusters which is quite similar to the EM algorithm. An extensive Monte Carlo
simulation study shows that our proposal compares favorably with other robust
and non robust model-based clustering procedures. We apply ours and alternative
procedures to a real data set and again find that the best results are obtained
using our proposal.
