A Divided Latent Class analysis for Big Data

被引：5

作者：

Abarda, Abdallah ^{[1
]}

Bentaleb, Youssef ^{[1
]}

Mharzi, Hassan ^{[1
]}

机构：

[1] Ibn Tofail Univ, Natl Sch Appl Sci, EECOMAS Lab, Kenitra, Morocco

来源：

14TH INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS AND PERVASIVE COMPUTING (MOBISPC 2017) / 12TH INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND COMMUNICATIONS (FNC 2017) / AFFILIATED WORKSHOPS | 2017年 / 110卷

关键词：

Latent class analysis; Massive data; Multivariate Categorical data; Statistical methods; Structural equation modeling;

D O I：

10.1016/j.procs.2017.06.111

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Statistical methods are a fundamental component in the big data environment. Among these methods: Latent class analysis (LCA), which is a subset of structural equation modeling, used to create classes in the case of multivariate categorical data. The use of this method to analyze massive data sets represents an expensive computational task. In this paper, we propose a Divide-and-Conquer approach for LCA model, the aim is to estimate the LCA parameters when this method is used for massive data sets. The performance of our approach will be verified by carrying out a numerical simulation. (C) 2017 The Authors. Published by Elsevier B.V.

引用

页码：428 / 433

页数：6

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