TY - GEN
T1 - Capturing Corporate Attributes in a New Perspective Through Fuzzy Clustering
AU - Matsumoto, Yusuke
AU - Suge, Aiko
AU - Takahashi, Hiroshi
N1 - Funding Information:
Acknowledgements. This research was supported by a grant-in-aid from the Kayamori Foundation of Informational Science Advancement.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Absolute prediction error
KW - Composite variance
KW - Fuzzy C Means
KW - Industrial classification
UR - http://www.scopus.com/inward/record.url?scp=85075550423&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075550423&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-31605-1_2
DO - 10.1007/978-3-030-31605-1_2
M3 - Conference contribution
AN - SCOPUS:85075550423
SN - 9783030316044
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 33
BT - New Frontiers in Artificial Intelligence - JSAI-isAI 2018 Workshops, JURISIN, AI-Biz, SKL, LENLS, IDAA, Revised Selected Papers
A2 - Kojima, Kazuhiro
A2 - Sakamoto, Maki
A2 - Mineshima, Koji
A2 - Satoh, Ken
PB - Springer
T2 - 10th International Symposium of Artificial Intelligence supported by the Japanese Society for Artificial Intelligence, JSAI-isAI 2018
Y2 - 12 November 2018 through 14 November 2018
ER -