@article{Liu2025, 
author = {Zhe Liu and Jiahao Shi and Dania Santina and Yulong Huang and Nabil Mlaiki},
title = {Auto-Weighted Neutrosophic Fuzzy Clustering for Multi-View Data},
year = {2025},
journal = {Computer Modeling in Engineering & Sciences},
volume = {144},
number = {3},
pages = {3531-3555},
keywords = {Multi-view data, neutrosophic fuzzy clustering, view weight, feature weight, uncertainty},
url = {https://www.sciopen.com/article/10.32604/cmes.2025.071145},
doi = {10.32604/cmes.2025.071145},
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.}
}