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

Yuchi Kanzawa

doi:10.1109/FUZZ-IEEE.2015.7337853

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

Yuchi Kanzawa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 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 language	English
Title of host publication	FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Volume	2015-November
ISBN (Electronic)	9781467374286
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2015.7337853
Publication status	Published - 2015 Nov 25
Event	IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey Duration: 2015 Aug 2 → 2015 Aug 5

Other

Other	IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
Country	Turkey
City	Istanbul
Period	15/8/2 → 15/8/5

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

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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

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