Open Access

ARTICLE

Multi-View Picture Fuzzy Clustering: A Novel Method for Partitioning Multi-View Relational Data

Pham Huy Thong¹, Hoang Thi Canh^2,3,*, Luong Thi Hong Lan⁴, Nguyen Tuan Huy⁴, Nguyen Long Giang^1,*

1 Insitute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, 100000, Vietnam
2 Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, 100000, Vietnam
3 Faculty of Information Technology, Thai Nguyen University of Information and Communication Technology, Thai Nguyen, 250000, Vietnam
4 School of Information and Communications Technology, Hanoi University of Industry, Hanoi, 100000, Vietnam

* Corresponding Authors: Hoang Thi Canh. Email: ; Nguyen Long Giang. Email:

Computers, Materials & Continua 2025, 83(3), 5461-5485. https://doi.org/10.32604/cmc.2025.065127

Received 04 March 2025; Accepted 18 April 2025; Issue published 19 May 2025

Abstract

Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex, high-dimensional data that single-view methods cannot capture. Traditional fuzzy clustering techniques, such as Fuzzy C-Means (FCM), face significant challenges in handling uncertainty and the dependencies between different views. To overcome these limitations, we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data, aiming to enhance clustering accuracy and robustness, termed Multi-view Picture Fuzzy Clustering (MPFC). In particular, the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels: membership degrees, neutral degrees, and refusal degrees. This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models. Meanwhile, dual-anchor graphs exploit the similarity relationships between data points and integrate information across views. This combination improves stability, scalability, and robustness when handling noisy and heterogeneous data. Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency, outperforming traditional methods. Specifically, the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets, attaining a Purity (PUR) score of 0.6440 and an Accuracy (ACC) score of 0.6213 for the 3Sources dataset, underscoring its robustness and efficiency. The proposed approach significantly contributes to fields such as pattern recognition, multi-view relational data analysis, and large-scale clustering problems. Future work will focus on extending the method for semi-supervised multi-view clustering, aiming to enhance adaptability, scalability, and performance in real-world applications.

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