On possibilistic clustering methods based on Shannon/Tsallis-entropy for spherical data and categorical multivariate data

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

13 Citations (Scopus)

Abstract

In this paper, four possibilistic clustering methods are proposed. First, we propose two possibilistic clustering methods for spherical data — one based on Shannon entropy, and the other on Tsallis entropy. These methods are derived by subtracting the cosine correlation between an object and a cluster center from 1, to obtain the object-cluster dissimilarity. These methods are derived from the proposed spherical data methods by considering analogies between the spherical and categorical multivariate fuzzy clustering methods, in which the fuzzy methods’ object-cluster similarity calculation is modified to accommodate the proposed possibilistic methods. The validity of the proposed methods is verified through numerical examples.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages115-128
Number of pages14
Volume9321
ISBN (Print)9783319232393
DOIs
Publication statusPublished - 2015
Event12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015 - Skovde, Sweden
Duration: 2015 Sep 212015 Sep 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9321
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015
CountrySweden
CitySkovde
Period15/9/2115/9/23

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Keywords

  • Categorical multivariate data
  • Possibilistic clustering
  • Spherical data

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kanzawa, Y. (2015). On possibilistic clustering methods based on Shannon/Tsallis-entropy for spherical data and categorical multivariate data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9321, pp. 115-128). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9321). Springer Verlag. https://doi.org/10.1007/978-3-319-23240-9_10