Model-based co-clustering for mixed type data

Selosse, Margot; Jacques, Julien; Biernacki, Christophe

Document type :

Article dans une revue scientifique

DOI :

10.1016/j.csda.2019.106866

Permalink :

http://hdl.handle.net/20.500.12210/29382

Title :

Model-based co-clustering for mixed type data

Author(s) :

Selosse, Margot [Auteur]
Jacques, Julien [Auteur]
Biernacki, Christophe [Auteur]

Journal title :

Computational Statistics and Data Analysis

Volume number :

144

Pages :

106866

Publisher :

Elsevier

Publication date :

2020

ISSN :

0167-9473

Keyword(s) :

Latent block model Corresponding author
Co-clustering
Mixed-type data
Latent block model

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]

English abstract : [en]

The importance of clustering for creating groups of observations is well known. The emergence of high-dimensional data sets with a huge number of features leads to co-clustering techniques, and several methods have been ...
Show more >The importance of clustering for creating groups of observations is well known. The emergence of high-dimensional data sets with a huge number of features leads to co-clustering techniques, and several methods have been developed for simultaneously producing groups of observations and features. By grouping the data set into blocks (the crossing of a row-cluster and a column-cluster), these techniques can sometimes better summarize the data set and its inherent structure. The Latent Block Model (LBM) is a well-known method for performing co-clustering. However, recently, contexts with features of different types (here called mixed type data sets) are becoming more common. The LBM is not directly applicable to this kind of data set. Here a natural extension of the usual LBM to the ``Multiple Latent Block Model" (MLBM) is proposed in order to handle mixed type data sets. Inference is performed using a Stochastic EM-algorithm that embeds a Gibbs sampler, and allows for missing data situations. A model selection criterion is defined to choose the number of row and column clusters. The method is then applied to both simulated and real data sets.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Collections :

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

Submission date :

2020-06-08T14:11:26Z

Version	Link	Modification date
2	20.500.12210/29382	2020-06-09T09:17:55Z
1	20.500.12210/29382.1*	2020-06-08T14:11:26Z

Model-based co-clustering for mixed type data

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Model-based co-clustering for mixed type data

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