Capturing Corporate Attributes in a New Perspective Through Fuzzy Clustering

Yusuke Matsumoto, Aiko Suge, Hiroshi Takahashi

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

1 Citation (Scopus)

Abstract

Although industrial classification plays an important role in various contexts, it is rarely questioned. However, as diversification and business transformation are ongoing, it is becoming difficult to recognize company’s real business. Therefore, there is not enough to allocate one type of business class to express the situation of the company, and a new type of industrial classification system is required. Through the analysis, we construct a new industrial classification system with Fuzzy C Means (FCM). This study also confirms the validity of proposed method through composite variance and absolute prediction error (APE). As the result, we present that there is a possibility that we are able to represent one company with overlapping industry, so to speak, assign one company more than two industries.

Original languageEnglish
Title of host publicationNew Frontiers in Artificial Intelligence - JSAI-isAI 2018 Workshops, JURISIN, AI-Biz, SKL, LENLS, IDAA, Revised Selected Papers
EditorsKazuhiro Kojima, Maki Sakamoto, Koji Mineshima, Ken Satoh
PublisherSpringer
Pages19-33
Number of pages15
ISBN (Print)9783030316044
DOIs
Publication statusPublished - 2019 Jan 1
Event10th International Symposium of Artificial Intelligence supported by the Japanese Society for Artificial Intelligence, JSAI-isAI 2018 - Yokohama, Japan
Duration: 2018 Nov 122018 Nov 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11717 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Symposium of Artificial Intelligence supported by the Japanese Society for Artificial Intelligence, JSAI-isAI 2018
CountryJapan
CityYokohama
Period18/11/1218/11/14

Keywords

  • Absolute prediction error
  • Composite variance
  • Fuzzy C Means
  • Industrial classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Matsumoto, Y., Suge, A., & Takahashi, H. (2019). Capturing Corporate Attributes in a New Perspective Through Fuzzy Clustering. In K. Kojima, M. Sakamoto, K. Mineshima, & K. Satoh (Eds.), New Frontiers in Artificial Intelligence - JSAI-isAI 2018 Workshops, JURISIN, AI-Biz, SKL, LENLS, IDAA, Revised Selected Papers (pp. 19-33). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11717 LNAI). Springer. https://doi.org/10.1007/978-3-030-31605-1_2