@Article{cmes.2025.071145,
AUTHOR = {Zhe Liu, Jiahao Shi, Dania Santina, Yulong Huang, Nabil Mlaiki},
TITLE = {Auto-Weighted Neutrosophic Fuzzy Clustering for Multi-View Data},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {144},
YEAR = {2025},
NUMBER = {3},
PAGES = {3531--3555},
URL = {http://www.techscience.com/CMES/v144n3/63976},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2025.071145}
}