RT Journal Article
A1 Yun Liu
AD College of Communication Engineering, Jilin University, Changchun, People's Republic of China
A1 Tao Hou
AD College of Communication Engineering, Jilin University, Changchun, People's Republic of China
A1 Fu Liu
AD College of Communication Engineering, Jilin University, Changchun, People's Republic of China

PB iet
T1 Improving fuzzy c-means method for unbalanced dataset
JN Electronics Letters
VO 51
IS 23
SP 1880
OP 1882
AB Traditional fuzzy c-means method (FCM) is a famous clustering algorithm, but has a poor clustering performance for unbalanced dataset. To tackle this defect, a new FCM is presented by introducing cluster size into the formula of determining the membership values in every iteration. Experimental results on synthetic and UCI datasets showed that the proposed method has a better clustering performance than traditional FCM in terms of dealing with datasets with unbalanced clusters.
K1 UCI datasets
K1 clustering algorithm
K1 membership values
K1 unbalanced dataset
K1 unbalanced clusters
K1 iteration methods
K1 FCM
K1 fuzzy C-means method
DO https://doi.org/10.1049/el.2015.1541
UL https://digital-library.theiet.org/;jsessionid=3klk2j0bj1fgs.x-iet-live-01content/journals/10.1049/el.2015.1541
LA English
SN 0013-5194
YR 2015
OL EN