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In this
paper we propose a hybrid clustering method that combines
the
strengths of bottom-up hierarchical clustering with that
of
top-down clustering. The first method is good at identifying
small clusters but not large ones; the strengths are reversed
for
the second method. The hybrid method is built on the new
idea of a mutual cluster: a group of points closer to each other
than to any other points. Theoretical connections between mutual
clusters and bottom-up clustering methods are established, aiding
in
their interpretation and providing an algorithm for identification
of
mutual clusters. We illustrate the technique on simulated
and
real microarray datasets.
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