On fuzzy clustering algorithms for nominal data

研究成果: Conference contribution

抄録

This paper presents three fuzzy clustering algorithms for nominal data. The first algorithm is similar to a conventional algorithm for vectorial data developed by introducing variables for controlling the cluster size. The second algorithm is similar to a conventional algorithm for vectorial data developed by regularizing another conventional algorithm for vectorial data with Kullback-Leibler divergence. The third algorithm is developed by regularizing the first algorithm mentioned above with q-divergence. Finally, some numerical experiments are conducted to investigate the features of the proposed algorithms.

本文言語English
ホスト出版物のタイトル2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728197326
DOI
出版ステータスPublished - 2020 12 5
イベントJoint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020 - Virtual, Tokyo, Japan
継続期間: 2020 12 52020 12 8

出版物シリーズ

名前2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020

Conference

ConferenceJoint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
CountryJapan
CityVirtual, Tokyo
Period20/12/520/12/8

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software
  • Computational Mathematics

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