Fuzzy clustering methods for categorical multivariate data based on q-divergence

Tadafumi Kondo, Yuchi Kanzawa

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper presents two fuzzy clustering algorithms for categorical multivariate data based on qdivergence. First, this study shows that a conventional method for vectorial data can be explained as regularizing another conventional method using qdivergence. Second, based on the known results that Kullback-Leibler (KL)-divergence is generalized into the q-divergence, and two conventional fuzzy clustering methods for categorical multivariate data adopt KL-divergence, two fuzzy clustering algorithms for categorical multivariate data that are based on qdivergence are derived from two optimization problems built by extending the KL-divergence in these conventional methods to the q-divergence. Through numerical experiments using real datasets, the proposed methods outperform the conventional methods in term of clustering accuracy.

Original languageEnglish
Pages (from-to)524-536
Number of pages13
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume22
Issue number4
DOIs
Publication statusPublished - 2018 Jul 1

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Keywords

  • Categorical multivariate data
  • Fuzzy clustering
  • KL-divergence
  • Q-divergence

ASJC Scopus subject areas

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

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