Unifying Data Units and Models in (Co-)Clustering

Biernacki, Christophe; Lourme, Alexandre

Document type :

Article dans une revue scientifique

Permalink :

http://hdl.handle.net/20.500.12210/29350

Title :

Unifying Data Units and Models in (Co-)Clustering

Author(s) :

Biernacki, Christophe [Auteur]

Lourme, Alexandre [Auteur]

Journal title :

Advances in Data Analysis and Classification

Volume number :

Publisher :

Springer Verlag

Publication date :

2018-05-25

ISSN :

1862-5347

Keyword(s) :

Measurement units
Mixed data
Mixture models
Model selection
Non-identifiability

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

Audience :

Internationale

Popular science :

Non

Collections :

METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2020-06-08T14:11:13Z
2020-06-09T09:23:24Z

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2	20.500.12210/29350*	2020-06-09T09:23:07Z
1	20.500.12210/29350.1	2020-06-08T14:11:13Z

Unifying Data Units and Models in (Co-)Clustering

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Unifying Data Units and Models in (Co-)Clustering

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