Unifying Data Units and Models in (Co-)Clustering

Biernacki, Christophe; Lourme, Alexandre

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1007/s11634-018-0325-2

Title :

Unifying Data Units and Models in (Co-)Clustering

Author(s) :

Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Lourme, Alexandre [Auteur]
MOdel for Data Analysis and Learning [MODAL]

Journal title :

Advances in Data Analysis and Classification

Pages :

7-31

Publisher :

Springer Verlag

Publication date :

2018-05-25

ISSN :

1862-5347

English keyword(s) :

Mixed data
Mixture models
Model selection
Non-identifiability
Measurement units

HAL domain(s) :

Statistiques [stat]/Méthodologie [stat.ME]

English abstract : [en]

Statisticians are already aware that any modelling process issue (exploration, prediction) is wholly data unit dependent, to the extend that it should be impossible to provide a statistical outcome without specifying the ...
Show more >Statisticians are already aware that any modelling process issue (exploration, prediction) is wholly data unit dependent, to the extend that it should be impossible to provide a statistical outcome without specifying the couple (unit,model). In this work, this general principle is formalized with a particular focus in model-based clustering and co-clustering in the case of possibly mixed data types (continuous and/or categorical and/or counting features), being also the opportunity to revisit what the related data units are. Such a formalization allows to raise three important spots: (i) the couple (unit,model) is not identifiable so that different interpretations unit/model of the same whole modelling process are always possible; (ii) combining different " classical " units with different " classical " models should be an interesting opportunity for a cheap, wide and meaningful enlarging of the whole modelling process family designed by the couple (unit,model); (iii) if necessary, this couple , up to the non identifiability property, could be selected by any traditional model selection criterion. Some experiments on real data sets illustrate in detail practical benefits from the previous three spots.Show less >

Language :

Anglais

Popular science :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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