Knowledge discovery and modeling based on Conditional Fuzzy Clustering with Interval Type-2 fuzzy

This paper is concerned with a method for designing improved Linguistic Model (LM) using Conditional Fuzzy Clustering (CFC) with two different Interval Type-2 (IT2) fuzzy approaches. The fuzzification factor and contexts with IT2 fuzzy approach are used to deal with uncertainty of clustering. This proposed clustering technique has characteristics that estimate the prototypes by preserving the homogeneity between the clustered patterns from the IT2-based contexts, and controls the amount of fuzziness of fuzzy c-partition. Thus, the proposed method can represent a nonlinear and complex characteristic more effectively than conventional LM. The experimental partial results on coagulant dosing process in a water purification plant revealed that the proposed method showed a better performance in comparison to the previous works.