  It is well known that the classical single linkage algorithm usually fails to
identify clusters in the presence of outliers. In this paper, we propose a new
version of this algorithm, and we study its mathematical performances. In
particular, we establish an oracle type inequality which ensures that our
procedure allows to recover the clusters with large probability under minimal
assumptions on the distribution of the outliers. We deduce from this inequality
the consistency and some rates of convergence of our algorithm for various
situations. Performances of our approach is also assessed through simulation
studies and a comparison with classical clustering algorithms on simulated data
is also presented.
