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Model-Based Co-clustering for Functional Data

Yosra Slimen 1, 2 Sylvain Allio 1 Julien Jacques 3, 2
3 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : In order to provide a simplified representation of key performance indicators for an easier analysis by mobile network maintainers, a model-based co-clustering algorithm for functional data is proposed. Co-clustering aims to identify block patterns in a data set from a simultaneous clustering of rows and columns. The algorithm relies on the latent block model in which each curve is identified by its functional principal components that are modeled by a multivariate Gaussian distribution whose parameters are block-specific. These latter are estimated by a stochastic EM algorithm embedding a Gibbs sampling. In order to select the numbers of row-and column-clusters, an ICL-BIC criterion is introduced. In addition to be the first co-clustering algorithm for functional data, the advantage of the proposed model is its ability to extract the hidden double structure induced by the data and its ability to deal with missing values. The model has proven its efficiency on simulated data and on a real data application that helps to optimize the topology of 4G mobile networks.
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Contributor : Julien Jacques <>
Submitted on : Monday, December 26, 2016 - 10:08:27 PM
Last modification on : Tuesday, December 15, 2020 - 4:51:48 PM
Long-term archiving on: : Tuesday, March 21, 2017 - 1:15:59 PM


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Yosra Slimen, Sylvain Allio, Julien Jacques. Model-Based Co-clustering for Functional Data. Neurocomputing, Elsevier, 2018, 291, pp.97-108. ⟨10.1016/j.neucom.2018.02.055⟩. ⟨hal-01422756⟩



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