Tsallis Entropy-Regularized Fuzzy Classification Maximum Likelihood Clustering with a t-Distribution

This study proposes a fuzzy clustering algorithm based on fuzzy classification maximum likelihood, t-distribution, and Tsallis entropy regularization. The proposed algorithm is shown to be a generalization of the two conventional algorithms, not only in the use of their objective functions, but also...

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Bibliographic Details
Published inJournal of Advanced Computational Intelligence and Intelligent Informatics Vol. 29; no. 2; pp. 365 - 378
Main Authors Suzuki, Yuta, Kanzawa, Yuchi
Format Journal Article
LanguageEnglish
Published Tokyo Fuji Technology Press Ltd 20.03.2025
富士技術出版株式会社
Fuji Technology Press Co. Ltd
Subjects
Accuracy
Algorithms
Classification
cluster-wise anisotropic data
Clustering
Datasets
Entropy
Experiments
fuzzy clustering
Informatics
Probability distribution
Regularization
t-distribution
Tsallis entropy-regularization
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Summary:This study proposes a fuzzy clustering algorithm based on fuzzy classification maximum likelihood, t-distribution, and Tsallis entropy regularization. The proposed algorithm is shown to be a generalization of the two conventional algorithms, not only in the use of their objective functions, but also at their algorithmic level. The robustness of the proposed algorithm to outliers was confirmed in numerical experiments using an artificial dataset. In addition, experiments using 11 real datasets demonstrated the superiority of proposed algorithm in terms of the clustering accuracy.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p0365