Abstract
Clustering algorithms are now in widespread use for
sorting heterogeneous data into homogeneous blocks. If
the data consist of a number of variables taking values
over a number of cases, these algorithms may be used
either to construct clusters of variables (using, say,
correlation as a measure of distance between variables)
or clusters of cases. This article presents a model,
and a technique, for clustering cases and variables
simultaneously. The principal advantage in this
approach is the direct interpretation of the clusters
on the data.
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