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

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Abstract

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.

Original languageEnglish
Pages (from-to)784-792
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume16
Issue number7
Publication statusPublished - 2012 Nov

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Keywords

  • Entropy-regularized fuzzy c-means
  • Indefinite kernel
  • Non- Euclidean relational data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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