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

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2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)511-519
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume17
Issue number4
Publication statusPublished - 2013 Jul

Keywords

  • Indefinite kernel
  • Kernel fuzzy clustering
  • Non-Euclidean relational data
  • Object-wise β -spread transformation
  • Relational fuzzy clustering

ASJC Scopus subject areas

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

Cite this

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abstract = "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.",
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