J. Hartigan. Journal of the American Statistical Association, 67 (337):
123--129(1972)
DOI: 10.2307/2284710
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.
%0 Journal Article
%1 hartigan-direct-clustering-data-1972
%A Hartigan, J. A.
%D 1972
%I American Statistical Association
%J Journal of the American Statistical Association
%K biclustering clustering
%N 337
%P 123--129
%R 10.2307/2284710
%T Direct Clustering of a Data Matrix
%U http://dx.doi.org/10.2307/2284710
%V 67
%X 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.
@article{hartigan-direct-clustering-data-1972,
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.}},
added-at = {2011-10-20T15:53:06.000+0200},
author = {Hartigan, J. A.},
biburl = {https://www.bibsonomy.org/bibtex/210d3765e07cfc30c5c6a6fa6007497a9/mhwombat},
citeulike-article-id = {3519125},
citeulike-linkout-0 = {http://dx.doi.org/10.2307/2284710},
citeulike-linkout-1 = {http://www.jstor.org/stable/2284710},
doi = {10.2307/2284710},
interhash = {87e815a20af852b0bac67e4c12e3a85f},
intrahash = {10d3765e07cfc30c5c6a6fa6007497a9},
issn = {01621459},
journal = {Journal of the American Statistical Association},
keywords = {biclustering clustering},
number = 337,
pages = {123--129},
posted-at = {2008-11-15 17:20:45},
priority = {2},
publisher = {American Statistical Association},
timestamp = {2016-07-12T19:25:30.000+0200},
title = {{Direct Clustering of a Data Matrix}},
url = {http://dx.doi.org/10.2307/2284710},
volume = 67,
year = 1972
}