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HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

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HAL Id: hal-00717506

https://hal.archives-ouvertes.fr/hal-00717506

Submitted on 13 Jul 2012

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HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

Laurent Bergé, Charles Bouveyron, Stéphane Girard

To cite this version:

Laurent Bergé, Charles Bouveyron, Stéphane Girard. HDclassif: An R Package for Model-Based Clus- tering and Discriminant Analysis of High-Dimensional Data. 1ères Rencontres R, Jul 2012, Bordeaux, France. �hal-00717506�

(2)

and Disriminant Analysis of High-Dimensional Data

L. Bergé

a

and C. Bouveyron

b

and S. Girard

c

a

LaboratoireGREthA

Université Bordeaux IV

laurent.bergeu-bordeaux4.fr

b

LaboratoireSAMM, EA 4543

Université Paris1 Panthéon-Sorbonne

harles.bouveyronuniv-paris1.fr

b

Equipe Mistis

INRIA Rhne-Alpes &LJK

stephane.girardinrialpes.fr

Mots lefs : Model-based lassiationand lustering, high-dimensional data, subspaes.

ThispaperpresentstheRpakageHDlassif whihisdevotedtothelusteringandthedisrim-

inant analysis of high-dimensional data. The lassiation methods proposed in the pakage

result from a new parametrization of the Gaussianmixture model whih ombines the idea of

dimension redution and model onstraints on the ovariane matries. The supervised las-

siation method using this parametrization is alled high dimensional disriminant analysis

(HDDA).Inasimilarmanner,the assoiatedlusteringmethodisalledhighdimensionaldata

lustering (HDDC)and usesthe expetation-maximizationalgorithmforinferene. Inorderto

orretlytthe data,both methodsestimatethe speisubspae and the intrinsi dimension

of the groups. Due to the onstraints on the ovariane matries, the number of parameters

toestimateissigniantly lowerthan othermodel-based methodsand this allows the methods

to be stable and eient in high dimensions. Two introdutory examples illustrated with R

odes allow the user to disover the hdda and hdd funtions. Experiments onsimulated and

real datasets also ompare HDDC and HDDA with existing lassiation methods on high-

dimensional datasets. HDlassif is a free software and distributed under the general publi

liense, aspart of the R software projet.

TheRpakageHDlassif (urrentlyinversion1.2)implementsthesetwolassiationmethods

for the lustering and the disriminant analysis of high-dimensional data. The pakage is

availablefrom the CRAN athttp://CRAN.R-projets.org/pakage=HDlassif.

Référenes

[1℄L.Bergé, C.BouveyronandS.Girard,HDlassif: anRPakageforModel-BasedClustering

and Disriminant Analysis of High-Dimensional Data, Journal of Statistial Software, vol. 42

Références

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