Semi-Supervised Fuzzy Clustering Based on Prior Membership

Yinghan Hong, Guoxiang Zhong, Jiahao Lian (), Guizhen Mai, Honghong Zhou, Pinghua Chen, Junliu Zhong and Hui Cao
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Yinghan Hong: School of Artificial Intelligence, Guangzhou Maritime University, Guangzhou 510520, China
Guoxiang Zhong: Pengcheng Laboratory, Shenzhen 518000, China
Jiahao Lian: School of Computer, Guangdong University of Technology, Guangzhou 510006, China
Guizhen Mai: School of Artificial Intelligence, Guangzhou Maritime University, Guangzhou 510520, China
Honghong Zhou: Guangdong Science and Technology Innovation Monitoring and Research Center, Guangzhou 510030, China
Pinghua Chen: School of Computer, Guangdong University of Technology, Guangzhou 510006, China
Junliu Zhong: School of Artificial Intelligence, Guangzhou Maritime University, Guangzhou 510520, China
Hui Cao: School of Artificial Intelligence, Guangzhou Maritime University, Guangzhou 510520, China

Mathematics, 2025, vol. 13, issue 16, 1-18

Abstract: Traditional fuzzy clustering algorithms construct sample partition criteria solely based on similarity measures but lack an effective representation of prior membership information, which limits further improvements in clustering accuracy. To address this issue, this paper proposes a semi-supervised fuzzy clustering algorithm based on prior membership (SFCM-PM). The proposed algorithm introduces prior information entropy as a metric to quantify the divergence between partition membership and prior membership and incorporates this as an auxiliary partition criterion into the objective function. By jointly optimizing data similarity and consistency with prior knowledge during the clustering process, the algorithm achieves more accurate and reliable clustering results. The experimental results demonstrate that the SFCM-PM algorithm achieves significant performance improvements by incorporating a small number of prior membership samples across several standard and real-world datasets. It also performs outstandingly on datasets with unbalanced sample distributions.

Keywords: clustering; prior membership; prior information entropy; partition criterion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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