Advancements in intuitionistic fuzzy rough graphs

Rough sets and intuitionistic fuzzy (IF) sets are two separate mathematical frameworks designed to model and manage incomplete or uncertain knowledge. By integrating these models, an IF rough framework is constructed, offering enhanced expressiveness and flexibility for representing and processing incomplete data within information systems. In this paper, we introduce a new hybrid model utilizing minimal IF neighborhoods. This model, based on any two IF binary relations defined on a non-empty universe, leads to the development of two novel IF graph approximation spaces aimed at reducing the boundary region of fuzzy uncertainty and increasing the precision degree of the fuzzy approximations. Furthermore, key results pertaining to both types of IF graph approximations are established. The relationships between the existing IF approximation methods are derived, and comparisons are made to demonstrate that the proposed approaches are more general than previous models. Finally, we explore an application of these IF graph approximation spaces in decision-making contexts and propose an algorithm to facilitate solving such problems.