A Large-Scale Group Decision-Making Method Fusing Three-Way Clustering and Regret Theory Under Fuzzy Preference Relations

Computational intelligence is increasingly applied to complex decision-making challenges, leveraging its data analysis prowess. Hybrid human-artificial intelligence models enhance the grasp of intricate social behaviors, offering valuable insights for social computing and behavior modeling. Within this landscape, large-scale group decision-making (LSGDM) emerges as an invaluable asset for navigating intricate decision-making scenarios. LSGDM enlists the expertise of individuals who articulate their preferences via fuzzy preference relations that abide by additively consistent principles. Its ascendancy is underscored by its applicability and relevance in confronting multifarious decision-making conundrums. In the realm of LSGDM, machine learning methodologies, such as cluster analysis, are deployed to streamline decision-making procedures, particularly when confronted with inherent complexities. The consensus reaching process (CRP) serves as the cornerstone, ensuring that decision makers (DMs) converge on a unified verdict. Consequently, comprehensive exploration of cluster analysis and CRP assumes a pivotal role in elevating the effectiveness of LSGDM. To further augment LSGDM, this study leverages a three-way clustering approach grounded in adaptive fuzzy c-mean clustering. This stratagem categorizes DMs into discrete subgroups. Moreover, a consensus metric, embracing both cardinal and ordinal consensus considerations, is established. This metric serves as the foundation for computing DMs' intragroup weights and group weights. Moreover, this article introduces a feedback mechanism imbued with identification and modification rules (directional rules). It incorporates a modification function that takes into account the consensus threshold, DMs' regret psychology, and the consensus level. This modification function methodically derives modification parameters for the spectrum of DMs. Finally, the viability and effectiveness of the LSGDM methodology proffered in this article are substantiated via meticulous simulation and comparative analyses.