Unifying Data Units and Models in (Co-)Clustering - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Advances in Data Analysis and Classification Year : 2018

Unifying Data Units and Models in (Co-)Clustering

Abstract

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.
Fichier principal
Vignette du fichier
paper_units_biernacki_lourme.pdf (5.21 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01653881 , version 1 (02-12-2017)

Identifiers

Cite

Christophe Biernacki, Alexandre Lourme. Unifying Data Units and Models in (Co-)Clustering. Advances in Data Analysis and Classification, 2018, 13, pp.7-31. ⟨10.1007/s11634-018-0325-2⟩. ⟨hal-01653881⟩
302 View
146 Download

Altmetric

Share

Gmail Facebook X LinkedIn More