Research Article

A Weight Possibilistic Fuzzy C-Means Clustering Algorithm

Algorithm 1

Weight possibilistic fuzzy c-means clustering algorithm.
(1)Initializing parameters m (m > 1), q (q > 1), and ε, c (0 < c < 1), setting the maximum cycle number max_iter, setting the initial value of cycle number as 1, and randomly generating centroid V0.
(2)Computing distance according to
(3)Computing the weight parameter γij and (1−γij) by using equation (18)
(4)Computing membership value uij and typicality value tij by using equations (19) and (20)
(5)Computing the objective function obj_fcn
(6)If |obj_fcn (i)-obj_fcn (i−1) |<ε or iterative times are less than max_iter, then stop
 Else obj_fcn (i) ⟶ obj_fcn (i−1)
(7)Computing centroid by using equation (21) and going to step 2