Open Access

ARTICLE

An Innovative Semi-Supervised Fuzzy Clustering Technique Using Cluster Boundaries

Duong Tien Dung^1,2,3, Ha Hai Nam⁴, Nguyen Long Giang³, Luong Thi Hong Lan^5,*

1 Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, 100000, Vietnam
2 FPT Software Company Limited, Hanoi, 100000, Vietnam
3 Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, 100000, Vietnam
4 Faculty of Information Technology, Electric Power University, Hanoi, 100000, Vietnam
5 School of Information and Communications Technology, Hanoi University of Industry, Hanoi, 100000, Vietnam

* Corresponding Author: Luong Thi Hong Lan. Email:

(This article belongs to the Special Issue: Fuzzy Logic: Next-Generation Algorithms and Applications)

Computers, Materials & Continua 2025, 85(3), 5341-5357. https://doi.org/10.32604/cmc.2025.068299

Received 25 May 2025; Accepted 22 August 2025; Issue published 23 October 2025

Abstract

Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data, guided by active learning, to enhance classification accuracy, particularly in complex and ambiguous datasets. Although several active semi-supervised fuzzy clustering methods have been developed previously, they typically face significant limitations, including high computational complexity, sensitivity to initial cluster centroids, and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters. This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify, analyze, and correct misclassified boundary elements. By strategically utilizing labeled data through active learning, our method improves the robustness and precision of cluster boundary assignments. Extensive experimental evaluations conducted on three types of datasets—including benchmark UCI datasets, synthetic data with controlled boundary overlap, and satellite imagery—demonstrate that our proposed approach achieves superior performance in terms of clustering accuracy and robustness compared to existing active semi-supervised fuzzy clustering methods. The results confirm the effectiveness and practicality of our method in handling real-world scenarios where precise cluster boundaries are critical.

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Keywords

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