Fuzzy c-means clustering for data with tolerance using kernel functions

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, two new clustering algorithms based on fuzzy c-means for data with tolerance are proposed. Kernel functions which map the data from the original space into higher dimensional feature space are introduced into the proposed algorithms. Nonlinear boundary of clusters can be easily found by using the kernel functions. First, two clustering algorithms for data with tolerance are introduced. One is based on standard method and the other is on entropy-based one. Second, two objective functions in feature space are shown corresponding to two methods, respectively. Third, Karush-Kuhn-Tucker conditions of two objective functions are considered, respectively, and these conditions are reexpressed with kernel functions as the representation of an inner product for mapping from original pattern space into higher dimensional feature space than the original one. Last, two iterative algorithms are proposed for the objective functions, respectively.

本文言語English
ホスト出版物のタイトル2006 IEEE International Conference on Fuzzy Systems
ページ744-750
ページ数7
DOI
出版ステータスPublished - 2006 12 1
イベント2006 IEEE International Conference on Fuzzy Systems - Vancouver, BC, Canada
継続期間: 2006 7 162006 7 21

出版物シリーズ

名前IEEE International Conference on Fuzzy Systems
ISSN(印刷版)1098-7584

Conference

Conference2006 IEEE International Conference on Fuzzy Systems
国/地域Canada
CityVancouver, BC
Period06/7/1606/7/21

ASJC Scopus subject areas

  • ソフトウェア
  • 理論的コンピュータサイエンス
  • 人工知能
  • 応用数学

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