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Clustering objects on subsets of attributes (with discussion)
Author(s) -
Friedman Jerome H.,
Meulman Jacqueline J.
Publication year - 2004
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2004.02059.x
Subject(s) - cluster analysis , cardinality (data modeling) , computer science , single linkage clustering , data mining , cluster (spacecraft) , correlation clustering , conjunction (astronomy) , complete linkage clustering , cure data clustering algorithm , artificial intelligence , pattern recognition (psychology) , programming language , physics , astronomy
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.