Open Access

ARTICLE

Auto-Weighted Neutrosophic Fuzzy Clustering for Multi-View Data

Zhe Liu^1,2,*, Jiahao Shi³, Dania Santina⁴, Yulong Huang¹, Nabil Mlaiki⁴

1 College of Mathematics and Computer, Xinyu University, Xinyu, 338004, China
2 School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia
3 College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China
4 Department of Mathematics and Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

* Corresponding Author: Zhe Liu. Email:

(This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)

Computer Modeling in Engineering & Sciences 2025, 144(3), 3531-3555. https://doi.org/10.32604/cmes.2025.071145

Received 01 August 2025; Accepted 03 September 2025; Issue published 30 September 2025

Abstract

The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations. However, traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data, as they rely on a single-dimensional membership value. To overcome these limitations, we propose an auto-weighted multi-view neutrosophic fuzzy clustering (AW-MVNFC) algorithm. Our method leverages the neutrosophic framework, an extension of fuzzy sets, to explicitly model imprecision and ambiguity through three membership degrees. The core novelty of AW-MVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view. Through a unified objective function, AW-MVNFC jointly optimizes the neutrosophic membership assignments, cluster centers, and the distributions of view and feature weights. Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods, demonstrating its effectiveness in handling the complexities of multi-view data.

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Keywords

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