Clustering Objects on Subsets of Attributes (with Discussion)
Journal of the Royal Statistical Society Series B (Statistical Methodology)Published 13 October 2004Open access
Jerome H. Friedman, Jacqueline J. Meulman
Citations427
SJR quartileQ1
SJR score3.31
SNIP2.48
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TL;DR
A new procedure is proposed for clustering attribute value data that encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously.
Abstract
Summary A new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.
Keywords
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