  We give a constant factor polynomial time pseudo-approximation algorithm for
min-sum clustering with or without outliers. The algorithm is allowed to
exclude an arbitrarily small constant fraction of the points. For instance, we
show how to compute a solution that clusters 98\% of the input data points and
pays no more than a constant factor times the optimal solution that clusters
99\% of the input data points. More generally, we give the following bicriteria
approximation: For any $\eps > 0$, for any instance with $n$ input points and
for any positive integer $n'\le n$, we compute in polynomial time a clustering
of at least $(1-\eps) n'$ points of cost at most a constant factor greater than
the optimal cost of clustering $n'$ points. The approximation guarantee grows
with $\frac{1}{\eps}$. Our results apply to instances of points in real space
endowed with squared Euclidean distance, as well as to points in a metric
space, where the number of clusters, and also the dimension if relevant, is
arbitrary (part of the input, not an absolute constant).
