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Direct Clustering of a Data Matrix
J. A. Hartigan
Journal of the American Statistical Association
Vol. 67, No. 337 (Mar., 1972), pp. 123-129
Stable URL: http://www.jstor.org/stable/2284710
Page Count: 7
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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.
Journal of the American Statistical Association © 1972 American Statistical Association