On fuzzy clustering algorithms for nominal data

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

Abstract

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.

Original languageEnglish
Title of host publication2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728197326
DOIs
Publication statusPublished - 2020 Dec 5
EventJoint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020 - Virtual, Tokyo, Japan
Duration: 2020 Dec 52020 Dec 8

Publication series

Name2020 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

Keywords

  • Divergence
  • Fuzzy Clustering
  • Nominal Data

ASJC Scopus subject areas

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

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