Entropy-regularized fuzzy clustering for non-euclidean relational data and indefinite kernel data

研究成果: Article査読

8 被引用数 (Scopus)

抄録

In this paper, an entropy-regularized fuzzy clustering approach for non-Euclidean relational data and indefinite kernel data is developed that has not previously been discussed. It is important because relational data and kernel data are not always Euclidean and positive semi-definite, respectively. It is theoretically determined that an entropy-regularized approach for both non-Euclidean relational data and indefinite kernel data can be applied without using a β -spread transformation, and that two other options make the clustering results crisp for both data types. These results are in contrast to those from the standard approach. Numerical experiments are employed to verify the theoretical results, and the clustering accuracy of three entropy-regularized approaches for non-Euclidean relational data, and three for indefinite kernel data, is compared.

本文言語English
ページ(範囲)784-792
ページ数9
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
16
7
DOI
出版ステータスPublished - 2012 11

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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