Fuzzy classification function of entropy regularized fuzzy c-means algorithm for data with tolerance using kernel function

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

研究成果: Conference contribution

5 引用 (Scopus)

抜粋

In this paper, the fuzzy classification functions of the entropy regularized fuzzy c-means for data with tolerance using kernel functions are proposed. First, the standard clustering algorithm for data with tolerance using kernel functions are introduced. Second, the fuzzy classification function for fuzzy c-means without tolerance using kernel functions is discussed as the solution of a certain optimization problem. Third, the optimization problem is shown so that the solutions are the fuzzy classification function values for the entropy regularized fuzzy c-means algorithms using kernel functions with respect to data with tolerance. Fourth, Karush-Kuhn-Tucker conditions of the objective function is considered, and the iterative algorithm is proposed for the optimization problem. Some numerical examples are shown.

元の言語English
ホスト出版物のタイトル2008 IEEE International Conference on Granular Computing, GRC 2008
ページ350-355
ページ数6
DOI
出版物ステータスPublished - 2008 12 30
イベント2008 IEEE International Conference on Granular Computing, GRC 2008 - Hangzhou, China
継続期間: 2008 8 262008 8 28

出版物シリーズ

名前2008 IEEE International Conference on Granular Computing, GRC 2008

Conference

Conference2008 IEEE International Conference on Granular Computing, GRC 2008
China
Hangzhou
期間08/8/2608/8/28

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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  • これを引用

    Kanzawa, Y., Endo, Y., & Miyamoto, S. (2008). Fuzzy classification function of entropy regularized fuzzy c-means algorithm for data with tolerance using kernel function. : 2008 IEEE International Conference on Granular Computing, GRC 2008 (pp. 350-355). [4664765] (2008 IEEE International Conference on Granular Computing, GRC 2008). https://doi.org/10.1109/GRC.2008.4664765