Relational fuzzy c-means and kernel fuzzy c-means using an object-wise β -spread transformation

研究成果: Article査読

2 被引用数 (Scopus)

抄録

Clustering methods of relational data are often based on the assumption that a given set of relational data is Euclidean, and kernelized clustering methods are often based on the assumption that a given kernel is positive semidefinite. In practice, non-Euclidean relational data and an indefinite kernel may arise, and a β -spread transformation was proposed for such cases, which modified a given set of relational data or a give a kernel Gram matrix such that the modified β value is common to all objects. In this paper, we propose an object-wiseβ -spread transformation for use in both relational and kernelized fuzzy c-means clustering. The proposed system retains the given data better than conventional methods, and numerical examples show that our method is efficient for both relational and kernel fuzzy c-means.

本文言語English
ページ(範囲)511-519
ページ数9
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
17
4
DOI
出版ステータスPublished - 2013 7

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

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