| Issue |
|
RAIRO Oper. Res.
Volume 33,
Number 4,
October-December 1999
|
|
Page(s)
|
|
525 - 541 |
| DOI |
|
10.1051/ro:1999122 |
|
DOI: 10.1051/ro:1999122
RAIRO Rech. Opér. (vol. 33, n
4, 1999, pp. 525-541)
Classification croisée et modèles
Y. Bencheikh
Institut de Mathematiques, Université Ferhat Abbas de Setif, Setif
19000, Algérie.
Abstract:
The relations between automatic clustering methods and
inferentiel statistical models have mostely been studied when the data
involves only one set. We propose to study these relations in the case
of data
involving two sets. We shall look at cross clustering methods as
suggested by
Govaert [6]; we show that these methods, like the simple clustering
methods,
can be considered as a clustering approach of a mixture model. We
introduce
the notion of crossed mixture from a concret example and define the
notions of
likelihood and associated clustered likelihood. Then, we study the
relations
which exist between the crossed mixture models and simple models and we
show
that these relations are completely similar to those which exist between
the
crossed clustering methods and simple clustering methods.
Résumé:
Les liens existant entre les methodes de classification
automatique et les modeles de statistiques inferentielles ont surtout
ete etudies lorsque les donnees mettent en jeu un seul ensemble. Nous
nous
proposons ici de le faire lorsque les donnees mettent en jeu deux
ensembles.
Nous nous sommes interesses aux methodes de classification croisee
proposees
par Govaert [6]; nous montrons que ces methodes, comme les methodes de
classification simple, peuvent etre considerees, comme une approche
classification d'un modele de melange. Nous introduisons la notion de
melange
croise a partir d'un exemple concret et nous definissons les notions de
vraisemblance et de vraisemblance classifiante associees, nous etudions
ensuite les liens qui existent entre les modeles de melange croise et
les
modeles de melange simple et nous montrons que ces liens sont tout a
fait
analogues a ceux qui existent entre les methodes de classification
croisee et
les methodes de classification simple.
Keywords: L1 distance, automatic clustering, mixture, cross mixture.
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