Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

Model-Based Co-clustering for Ordinal Data

Julien Jacques 1, 2 Christophe Biernacki 3, 2 
2 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : A model-based co-clustering algorithm for ordinal data is presented. This algorithm relies on the latent block model embedding a probability distribution specific to ordinal data (the so-called BOS or Binary Ordinal Search distribution). Model inference relies on a Stochastic EM algorithm coupled with a Gibbs sampler, and the ICL-BIC criterion is used for selecting the number of co-clusters (or blocks). The main advantage of this ordinal dedicated co-clustering model is its parsimony, the interpretability of the co-cluster parameters (mode, precision) and the possibility to take into account missing data. Numerical experiments on simulated data show the efficiency of the inference strategy, and real data analyses illustrate the interest of the proposed procedure.
Document type :
Journal articles
Complete list of metadata

Cited literature [32 references]  Display  Hide  Download
Contributor : Julien Jacques Connect in order to contact the contributor
Submitted on : Thursday, September 28, 2017 - 12:46:06 AM
Last modification on : Friday, April 15, 2022 - 3:00:01 PM
Long-term archiving on: : Friday, December 29, 2017 - 12:26:38 PM


Files produced by the author(s)



Julien Jacques, Christophe Biernacki. Model-Based Co-clustering for Ordinal Data. Computational Statistics and Data Analysis, Elsevier, 2018, 123, pp.101-115. ⟨10.1016/j.csda.2018.01.014⟩. ⟨hal-01448299v2⟩



Record views


Files downloads