Q-divergence-based relational fuzzy C-means clustering

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1 Citation (Scopus)


In this paper, a clustering algorithm for relational data based on q-divergence between memberships and variables that control cluster sizes is proposed. A conventional method for vectorial data is first presented for interpretation as the regularization of another conventional method with q-divergence. With this interpretation, a clustering algorithm for relational data, based on q-divergence, is then derived from an optimization problem built by regularizing the conventional method with q-divergence. A theoretical discussion reveals the property of the proposed method. Numerical results are presented that substantiate this property and show that the proposed method outperforms two conventional methods in terms of accuracy.

Original languageEnglish
Pages (from-to)34-43
Number of pages10
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number1
Publication statusPublished - 2018 Jan


  • Fuzzy clustering
  • Q-divergence
  • Relational data

ASJC Scopus subject areas

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


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