Constructing intuitionistic neighborhood based on intuitionistic fuzzy sets for three-way clustering

Three-way clustering assigns highly uncertain samples to the boundary domains, effectively addressing the problem of misclassification caused by data uncertainty. In numerical attribute information systems, neighborhood rough sets can effectively capture the indiscernibility relations between objects. However, the conventional neighborhood relation suffers from a one-size-fits-all issue due to the fixed neighborhood radius. To solve these issues, we propose an intuitionistic neighborhood and construct a corresponding three-way clustering model. Specifically, we first capture the dual nature and uncertainty of neighborhood relations through the construction of the intuitionistic neighborhood. Then we construct a three-way clustering model with dual and single evaluation functions based on intuitionistic neighborhoods. Finally, we adaptively obtain the optimal threshold pairs by maximizing the clustering effectiveness index. Experiments conducted on twelve datasets demonstrate that our proposed method outperforms baseline methods, showing superior capability in handling the inherent uncertainty in information systems.