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

研究成果: Conference contribution

14 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版者Springer Verlag
ページ115-128
ページ数14
9321
ISBN(印刷物)9783319232393
DOI
出版物ステータスPublished - 2015
イベント12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015 - Skovde, Sweden
継続期間: 2015 9 212015 9 23

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9321
ISSN(印刷物)03029743
ISSN(電子版)16113349

Other

Other12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015
Sweden
Skovde
期間15/9/2115/9/23

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • これを引用

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