A Divided Latent Class analysis for Big Data | Zendy

Abdallah Abarda | Zendy; Youssef Bentaleb | Zendy; Hassan Mharzi | Zendy

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A Divided Latent Class analysis for Big Data

Author(s) -

Abdallah Abarda,

Youssef Bentaleb,

Hassan Mharzi

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.06.111

Subject(s) - computer science , categorical variable , class (philosophy) , big data , component (thermodynamics) , data mining , latent variable , latent class model , divide and conquer algorithms , task (project management) , multivariate statistics , structural equation modeling , machine learning , artificial intelligence , algorithm , physics , management , economics , thermodynamics

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.

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