Given a set of moving points in R^{d}, we show that
one can cluster them in advance, using a small number of clusters, so
that in any point in time this static clustering is competitive with
the optimal k-center clustering of the point-set at any point
in time. The advantage of this approach is that it avoids the usage of
kinetic data-structures and as such it does not need to update the
clustering as time passes.

To implement this static clustering efficiently, we describe a simple
technique for speeding-up clustering algorithms, and apply it to
achieve a faster clustering algorithms for several problems. In
particular, we present a linear time algorithm for computing a
2-approximation to the k-center clustering of a set of n
points in R^{d}. This slightly improves over the
algorithm of Feder and Greene [fg-oafac-88], that runs in
\Theta(n log k) time(which is optimal in the comparison model).