Intuitionistic Fuzzy Hybrid Data Mining-MAGDM Approach Using Correlated Aggregation for Solving Complex Decision Problems

Abstract

This paper addresses Multiple Attribute Group Decision Making (MAGDM) problems where attribute values are represented as Intuitionistic Fuzzy Sets (IFSs). A novel decision-making framework is developed by integrating the Choquet integral operator with Intuitionistic Fuzzy Ordered Weighted Averaging (IFOWA) operators, effectively capturing both the importance and ordered influence of attributes under uncertainty. A distinguishing feature of this work is the fusion of Data Mining techniques into the MAGDM model, enabling the identification and elimination of irrelevant or redundant attributes. This integration significantly enhances the model’s efficiency and decision quality by reducing complexity and focusing on the most impactful variables. The synergy between intuitionistic fuzzy logic and data mining provides a more intelligent and scalable decision-support system. The effectiveness and practicality of the proposed approach are demonstrated through a comprehensive numerical illustration.