Co-clustering for binary and functional data - Inria - Institut national de recherche en sciences et technologies du numérique
Journal Articles Communications in Statistics - Simulation and Computation Year : 2020

Co-clustering for binary and functional data

Abstract

Due to the diversity of mobile network technologies, the volume of data that has to be observed by mobile operators in a daily basis has become enormous. This huge volume has become an obstacle to mobile networks management. This paper aims to provide a simplified representation of these data for an easier analysis. A model-based co-clustering algorithm for mixed data, functional and binary, is therefore proposed. Co-clustering aims to identify block patterns in a dataset from a simultaneous clustering of rows and columns. The proposed approach relies on the latent block model, and three algorithms are compared for its inference: stochastic EM within Gibbs sampling, classification EM and variational EM. The proposed model is the first co-clustering algorithm for mixed data that deals with functional and binary features. The model has proven its efficiency on simulated data and on real data extracted from live 4G mobile networks.
Fichier principal
Vignette du fichier
co-clustering_mixed_data.pdf (1.1 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-02551245 , version 1 (22-04-2020)

Identifiers

Cite

Yosra Ben Slimen, Julien Jacques, Sylvain Allio. Co-clustering for binary and functional data. Communications in Statistics - Simulation and Computation, 2020, 51 (9), pp.4845-4866. ⟨10.1080/03610918.2020.1764033⟩. ⟨hal-02551245⟩
142 View
252 Download

Altmetric

Share

More