Co-clustering for binary and functional data

被引：1

作者：

Ben Slimen, Yosra ^{[1
,2
]}

Jacques, Julien ^{[1
]}

Allio, Sylvain ^{[2
]}

机构：

[1] Univ Lyon, ERIC EA3083, Lyon, France

[2] Orange Labs, Rech & Dev, Belfort, France

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2022年 / 51卷 / 09期

关键词：

Co-clustering; EM algorithm; functional data; ICL-BIC criterion; Latent block model; Mixed data; Mobile network; MODEL;

D O I：

10.1080/03610918.2020.1764033

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：4845 / 4866

页数：22

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