Multi-Granulation Intuitionistic Fuzzy Rough Sets Based on Principal Component Analysis

Granular computing is a multidisciplinary field that focuses on information processing models and their granular structure. However, there is still a lack of research on the construction of granularity for intuitionistic fuzzy information systems. In this paper, we propose two methods that utilize principal component analysis theory to create granularity structures for intuitionistic fuzzy systems. These approaches lead to the formation of a multi-granulation intuitionistic space and the construction of multi-granularity intuitionistic fuzzy rough set approximations. By using methods of this paper, we can decrease data redundancy for a more streamlined process. The experimental results indicate that our methods can improve the efficiency of information granulation.