On kernelization for a maximizing model of bezdek-like spherical fuzzy c-means clustering

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

In this study, we propose three modifications for a maximizing model of spherical Bezdek-type fuzzy c-means clustering (msbFCM). First, we kernelize msbFCM (K-msbFCM). The original msbFCM can only be applied to objects on the first quadrant of the unit hypersphere, whereas its kernelized form can be applied to a wider class of objects. The second modification is a spectral clustering approach to K-msbFCM using a certain assumption. This approach solves the local convergence problem in the original algorithm. The third modification is to construct a model providing the exact solution of the spectral clustering approach. Numerical examples demonstrate that the proposed methods can produce good results for clusters with nonlinear borders when an adequate parameter value is selected.

本文言語English
ホスト出版物のタイトルModeling Decisions forArtificial Intelligence - 11th International Conference, MDAI 2014, Proceedings
編集者Vicenç Torra, Yasuo Narukawa, Yasunori Endo
出版社Springer Verlag
ページ108-121
ページ数14
ISBN(電子版)9783319120539
DOI
出版ステータスPublished - 2014
イベント11th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2014 - Tokyo , Japan
継続期間: 2014 10 292014 10 31

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8825
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference11th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2014
国/地域Japan
CityTokyo
Period14/10/2914/10/31

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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