Fuzzy clustering based on α-divergence for spherical data and for categorical multivariate data

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

This paper presents two clustering algorithms based on α-divergence between memberships and variables that control cluster sizes: one is for spherical data and the other for categorical multivariate data. First, this paper shows that a conventional method for vectorial data can be interpreted as the regularization of another conventional method with α-divergence. Second, with this interpretation, a spherical clustering algorithm based on α-divergence is derived from an optimization problem built by regularizing a conventional method with α-divergence. Third, this paper connects the facts that the α-divergence is a generalization of Kullback-Leibler (KL)-divergence, and that three conventional co-clustering methods are based on KL-divergence. Based on these facts, a co-clustering algorithm based on α-divergence is derived from an optimization problem built by extending the KL-divergence in conventional methods to α-divergence. This paper also demonstrates some numerical examples for the proposed methods.

本文言語English
ホスト出版物のタイトルFUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
出版社Institute of Electrical and Electronics Engineers Inc.
2015-November
ISBN(電子版)9781467374286
DOI
出版ステータスPublished - 2015 11 25
イベントIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey
継続期間: 2015 8 22015 8 5

Other

OtherIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
国/地域Turkey
CityIstanbul
Period15/8/215/8/5

ASJC Scopus subject areas

  • ソフトウェア
  • 理論的コンピュータサイエンス
  • 人工知能
  • 応用数学

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