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Abstract:
The
information bottleneck
method is an unsupervised model independent data organization
technique. Given a joint distribution
P
(
A
,
B
), this method constructs a new variable
T
that extracts partitions, or clusters, over the values of
A
that are informative about
B
. In a recent paper, we introduced a general principled framework
for multivariate extensions of the information bottleneck method
that allows us to consider multiple systems of data partitions
that are inter-related. In this paper, we present a new family of
simple agglomerative algorithms to construct such systems of
inter-related clusters. We analyze the behavior of these
algorithms and apply them to several real-life datasets.
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