Multivariate clustering procedures with variable metrics

Several multivariate clustering methods are analyzed in which each cluster may have a different metric depending on its covariance matrix. Numerical experiments show that the only reliable method among these is one using a metric suggested by Rohlf [1970] based on the within cluster covariance matri...

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Detalles Bibliográficos
Autores principales: Maronna, R., Jacovkis, P.M.
Formato: JOUR
Materias:
cluster
discriminant analysis
methodology
multivariate analysis
statistics
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_0006341X_v30_n3_p499_Maronna
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_0006341X_v30_n3_p499_Maronna
record_format dspace
spelling todo:paper_0006341X_v30_n3_p499_Maronna2023-10-03T14:04:59Z Multivariate clustering procedures with variable metrics Maronna, R. Jacovkis, P.M. cluster discriminant analysis methodology multivariate analysis statistics Several multivariate clustering methods are analyzed in which each cluster may have a different metric depending on its covariance matrix. Numerical experiments show that the only reliable method among these is one using a metric suggested by Rohlf [1970] based on the within cluster covariance matrix normalized for unit determinant. (12 references.) JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_0006341X_v30_n3_p499_Maronna
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic cluster
discriminant analysis
methodology
multivariate analysis
statistics
spellingShingle cluster
discriminant analysis
methodology
multivariate analysis
statistics
Maronna, R.
Jacovkis, P.M.
Multivariate clustering procedures with variable metrics
topic_facet cluster
discriminant analysis
methodology
multivariate analysis
statistics
description Several multivariate clustering methods are analyzed in which each cluster may have a different metric depending on its covariance matrix. Numerical experiments show that the only reliable method among these is one using a metric suggested by Rohlf [1970] based on the within cluster covariance matrix normalized for unit determinant. (12 references.)
format JOUR
author Maronna, R.
Jacovkis, P.M.
author_facet Maronna, R.
Jacovkis, P.M.
author_sort Maronna, R.
title Multivariate clustering procedures with variable metrics
title_short Multivariate clustering procedures with variable metrics
title_full Multivariate clustering procedures with variable metrics
title_fullStr Multivariate clustering procedures with variable metrics
title_full_unstemmed Multivariate clustering procedures with variable metrics
title_sort multivariate clustering procedures with variable metrics
url http://hdl.handle.net/20.500.12110/paper_0006341X_v30_n3_p499_Maronna
work_keys_str_mv AT maronnar multivariateclusteringprocedureswithvariablemetrics
AT jacovkispm multivariateclusteringprocedureswithvariablemetrics
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