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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationFUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2015-November
ISBN (Electronic)9781467374286
DOIs
Publication statusPublished - 2015 Nov 25
EventIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey
Duration: 2015 Aug 22015 Aug 5

Other

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

Fingerprint

Fuzzy clustering
Clustering algorithms

Keywords

  • Atmospheric measurements
  • Clustering algorithms
  • Clustering methods
  • Entropy
  • Machine learning algorithms
  • Optimization
  • Particle measurements

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

Cite this

Kanzawa, Y. (2015). Fuzzy clustering based on α-divergence for spherical data and for categorical multivariate data. In FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems (Vol. 2015-November). [7337853] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2015.7337853

Fuzzy clustering based on α-divergence for spherical data and for categorical multivariate data. / Kanzawa, Yuchi.

FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. 7337853.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kanzawa, Y 2015, Fuzzy clustering based on α-divergence for spherical data and for categorical multivariate data. in FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. vol. 2015-November, 7337853, Institute of Electrical and Electronics Engineers Inc., IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015, Istanbul, Turkey, 15/8/2. https://doi.org/10.1109/FUZZ-IEEE.2015.7337853
Kanzawa Y. Fuzzy clustering based on α-divergence for spherical data and for categorical multivariate data. In FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. Vol. 2015-November. Institute of Electrical and Electronics Engineers Inc. 2015. 7337853 https://doi.org/10.1109/FUZZ-IEEE.2015.7337853
Kanzawa, Yuchi. / Fuzzy clustering based on α-divergence for spherical data and for categorical multivariate data. FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015.
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