Robust Interval Type-2 Possibilistic C-means Clustering and its Application for Fuzzy Modeling

This paper presents a robust interval type-2 possibilistic C-means (IT2PCM) clustering algorithm which is actually alternating cluster estimation, but membership functions are selected with interval type-2 fuzzy sets by the users. The cluster prototypes are calculated by type reduction combined with defuzzification; consequently they could be directly extracted to generate interval type-2 fuzzy rules that can be used to obtain a first approximation to the interval type-2 fuzzy logic system (IT2FLS). The proposed clustering algorithm is robust to uncertain inliers and outliers, at the same time provides a good initial structure of IT2FLS for further tuning in a subsequent process. Excellent simulation results are obtained for the problem of classification and forecasting.