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Journal Articles Advances in Data Analysis and Classification Year : 2018

Unifying Data Units and Models in (Co-)Clustering


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.
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Dates and versions

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



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⟩
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