Robust interval type-2 possibilistic C-means clustering
Zhou Cong · Kongzhi yu juece · 2009
This paper presents alternating iteration architecture for clustering, robust interval type-2 possibilistic C-means (IT2PCM) clustering algorithm. It is actually alternating cluster estimation, but membership functions are selected directly with interval type-2 fuzzy sets by the users. In the proposed method, the cluster prototype update equation is calculated by type reduction combined with defuzzification. It is robust to uncertain inliers and outliers on the basis of its φ function analysis in the framework of robust statistics. Simulation results of comparing IT2PCM with existing methods show the nice robust properties of IT2PCM.