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

Yuchi Kanzawa

doi:10.1007/978-3-319-23240-9_10

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

Yuchi Kanzawa

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Modeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings
Editors	Vicenç Torra, Yasuo Narukawa
Publisher	Springer Verlag
Pages	115-128
Number of pages	14
ISBN (Print)	9783319232393
DOIs	https://doi.org/10.1007/978-3-319-23240-9_10
Publication status	Published - 2015
Event	12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015 - Skovde, Sweden Duration: 2015 Sept 21 → 2015 Sept 23

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9321
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015
Country/Territory	Sweden
City	Skovde
Period	15/9/21 → 15/9/23

Keywords

Categorical multivariate data
Possibilistic clustering
Spherical data

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-23240-9_10

Cite this

Kanzawa, Y. (2015). On possibilistic clustering methods based on Shannon/Tsallis-entropy for spherical data and categorical multivariate data. In V. Torra, & Y. Narukawa (Eds.), Modeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings (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

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

Modeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings. ed. / Vicenç Torra; Yasuo Narukawa. Springer Verlag, 2015. p. 115-128 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9321).

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

Kanzawa, Y 2015, On possibilistic clustering methods based on Shannon/Tsallis-entropy for spherical data and categorical multivariate data. in V Torra & Y Narukawa (eds), Modeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9321, Springer Verlag, pp. 115-128, 12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015, Skovde, Sweden, 15/9/21. https://doi.org/10.1007/978-3-319-23240-9_10

Kanzawa Y. On possibilistic clustering methods based on Shannon/Tsallis-entropy for spherical data and categorical multivariate data. In Torra V, Narukawa Y, editors, Modeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings. Springer Verlag. 2015. p. 115-128. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23240-9_10

Kanzawa, Yuchi. / On possibilistic clustering methods based on Shannon/Tsallis-entropy for spherical data and categorical multivariate data. Modeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings. editor / Vicenç Torra ; Yasuo Narukawa. Springer Verlag, 2015. pp. 115-128 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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