The general objectives of this report are to provide a summary of the state-of-the-art in discriminant analysis and clustering and to identify key research and unsolved problems that need to be addressed in these two areas. It was prepared under the auspices of the Committee on Applied and Theoretical Statistics of the Board on Mathematical Sciences, National Research Council by its Panel on Discriminant Analysis, Classification, and Clustering. Both methodological and theoretical aspects are reviewed, and a survey of available software and algorithms is provided.
Publié le : 1989-02-14
Classification:
Agglomerative methods,
algorithms,
classification,
evolutionary distances,
high density clusters,
kernel methods,
logistic regression,
pattern recognition,
minimum spanning tree,
mixtures,
nearest neighbor methods,
single linkage,
complete linkage,
ultrametric distances,
variable selection,
software,
displays,
and diagnostics
@article{1177012666,
title = {Discriminant Analysis and Clustering: Panel on Discriminant Analysis, Classification, and Clustering},
journal = {Statist. Sci.},
volume = {4},
number = {4},
year = {1989},
pages = { 34-69},
language = {en},
url = {http://dml.mathdoc.fr/item/1177012666}
}
(éd.). Discriminant Analysis and Clustering: Panel on Discriminant Analysis, Classification, and Clustering. Statist. Sci., Tome 4 (1989) no. 4, pp. 34-69. http://gdmltest.u-ga.fr/item/1177012666/