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

Tadafumi Kondo, Yuchi Kanzawa

研究成果: Article

3 引用 (Scopus)

抜粋

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.

元の言語English
ページ(範囲)524-536
ページ数13
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
22
発行部数4
DOI
出版物ステータスPublished - 2018 7 1

ASJC Scopus subject areas

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

フィンガープリント Fuzzy clustering methods for categorical multivariate data based on q-divergence' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用