Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Advanced Computational Intelligence and Intelligent Informatics

DOI: 10.20965/jaciii.2025.p0365

Authors: Yuta Suzuki, Yuchi Kanzawa

Institution: Shibaura Institute of Technology

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4408614232

FWCI: 0.427

Cites: 9

Cited by: 1

Related to: 10