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

16 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 publicationModeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings
EditorsVicenç Torra, Yasuo Narukawa
PublisherSpringer Verlag
Pages115-128
Number of pages14
ISBN (Print)9783319232393
DOIs
Publication statusPublished - 2015
Event12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015 - Skovde, Sweden
Duration: 2015 Sept 212015 Sept 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9321
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

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

Keywords

  • Categorical multivariate data
  • Possibilistic clustering
  • Spherical data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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