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.

摘要

由于移动网络技术的多样性，移动运营商每天必须观察的数据量变得非常庞大。如此庞大的体量已成为移动网络管理的障碍。本文旨在提供这些数据的简化表示，以便于分析。因此，提出了一种用于混合数据、函数式和二进制数据的基于模型的协同聚类算法。联合聚类旨在从行和列的同时聚类中识别数据集中的块模式。所提出的方法依赖于潜在块模型，并比较了三种算法的推理：Gibbs 采样内的随机 EM、分类 EM 和变分 EM。所提出的模型是第一个处理功能和二进制特征的混合数据的联合聚类算法。