Latent Class Models on Business Analytics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper discusses the systematic application of the latent class model on business analytics. The latent class model is one of the effective statistical model classes on business analytics to represent essential statistical structures by learning the sparse and high dimensional data. This model class is useful for the purpose of reduction of feature dimension and cancellation of sparseness of the data. This is because many practical data can be assumed to consist of several unobserved subgroups. For example, a customers set, which is a target in the field of marketing analysis, consists of several subgroups with different characteristics and preferences. In addition, data clustering can also be realized by estimated belonging probabilities to latent classes of each data.This paper gives a general form of the latent class model and discuss how to construct the model structure and apply to a real problem in business analytics. After describing important points to be noted in analysis based on a latent class model, several practical examples are also shown.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
EditorsMotoi Iwashita, Atsushi Shimoda, Prajak Chertchom
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages142-147
Number of pages6
ISBN (Electronic)9781728108865
DOIs
Publication statusPublished - 2019 May
Event4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019 - Honolulu, United States
Duration: 2019 May 292019 May 31

Publication series

NameProceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019

Conference

Conference4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
CountryUnited States
CityHonolulu
Period19/5/2919/5/31

Keywords

  • Business Analytics
  • Latent Class Model
  • Machine Learning
  • Mixture Model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems and Management

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  • Cite this

    Goto, M. (2019). Latent Class Models on Business Analytics. In M. Iwashita, A. Shimoda, & P. Chertchom (Eds.), Proceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019 (pp. 142-147). [8885147] (Proceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCD.2019.8885147