Indefinite kernel fuzzy c-means clustering algorithms

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

This paper proposes two types of kernel fuzzy c-means algorithms with an indefinite kernel. Both algorithms are based on the fact that the relational fuzzy c-means algorithm is a special case of the kernel fuzzy c-means algorithm. The first proposed algorithm adaptively updated the indefinite kernel matrix such that the dissimilarity between each datum and each cluster center in the feature space is non-negative, instead of subtracting the minimal eigenvalue of the given kernel matrix as its preprocess. This derivation follows the manner in which the non-Euclidean relational fuzzy c-means algorithm is derived from the original relational fuzzy c-means one. The second proposed method produces the memberships by solving the optimization problem in which the constraint of non-negative memberships is added to the one of K-sFCM. This derivation follows the manner in which the non-Euclidean fuzzy relational clustering algorithm is derived from the original relational fuzzy c-means one. Through a numerical example, the proposed algorithms are discussed.

本文言語English
ホスト出版物のタイトルModeling Decisions for Artificial Intelligence - 7th International Conference, MDAI 2010, Proceedings
ページ116-128
ページ数13
DOI
出版ステータスPublished - 2010
イベント7th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2010 - Perpignan, France
継続期間: 2010 10 272010 10 29

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)
6408 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference7th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2010
CountryFrance
CityPerpignan
Period10/10/2710/10/29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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