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

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルModeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings
編集者Vicenç Torra, Yasuo Narukawa
出版社Springer Verlag
ページ115-128
ページ数14
ISBN(印刷版)9783319232393
DOI
出版ステータス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月 212015 9月 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(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

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

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

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