Latent Class Models on Business Analytics

Masayuki Goto*

*この研究の対応する著者

研究成果

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
編集者Motoi Iwashita, Atsushi Shimoda, Prajak Chertchom
出版社Institute of Electrical and Electronics Engineers Inc.
ページ142-147
ページ数6
ISBN(電子版)9781728108865
DOI
出版ステータスPublished - 2019 5
イベント4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019 - Honolulu, United States
継続期間: 2019 5 292019 5 31

出版物シリーズ

名前Proceedings - 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
国/地域United States
CityHonolulu
Period19/5/2919/5/31

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 情報システムおよび情報管理

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