A technique for forming information granules is shown in this paper. Based on the theory of uncertainty-based information, an approach toward a general base is given which forms information granules. Two implementations are proposed which form Interval Type-2 Fuzzy information granules, both with Takagi–Sugeno–Kang consequents optimized with Cuckoo search algorithm. These approaches capture multiple evaluations of uncertainty from taken samples and use these models to measure the uncertainty from the difference between them. The proposed approaches are tested with classification and curve identification datasets. 
Information granule formation via the concept of uncertainty-based information with Interval Type-2 Fuzzy Sets representation and Takagi–Sugeno–Kang consequents optimized with Cuckoo search