On Possibilistic Clustering Algorithms Based on Noise Clustering

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

1 Citation (Scopus)

Abstract

In this study, several possibilistic clustering methods are proposed based on noise clustering. The proposed methods are motivated by the fact that conventional possibilistic clustering methods do not correspond with noise clustering methods in entropy-regularized situations, whereas these methods do correspond in Bezdek-type fuzzified situation.

Original languageEnglish
Title of host publicationProceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages42-47
Number of pages6
ISBN (Electronic)9781467390415
DOIs
Publication statusPublished - 2016 Dec 28
Event8th Joint International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016 - Sapporo, Hokkaido, Japan
Duration: 2016 Aug 252016 Aug 28

Other

Other8th Joint International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016
CountryJapan
CitySapporo, Hokkaido
Period16/8/2516/8/28

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Keywords

  • Noise Clustering
  • Possibilistic Clustering

ASJC Scopus subject areas

  • Control and Optimization
  • Artificial Intelligence
  • Software
  • Computational Mathematics
  • Modelling and Simulation

Cite this

Kanzawa, Y. (2016). On Possibilistic Clustering Algorithms Based on Noise Clustering. In Proceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016 (pp. 42-47). [7801610] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCIS-ISIS.2016.0023