Clustering is increasing in importance, but linear- and even
constant-time clustering algorithms are often too slow for real-time
applications. A simple way to speed up clustering is to speed up the
distance calculations at the heart of clustering routines. We study
two techniques for improving the cost of distance calculations, {\em
LSI} and {\em truncation}, and determine both how much these
techniques speed up clustering and how much they affect the quality of
the resulting clusters. We find that the speed increase is
significant while -- surprisingly -- the quality of clustering is
not adversely affected. We conclude that truncation yields clusters
as good as those produced by full-profile clustering while offering a
significant speed advantage.