%0 Electronic Article %A Yun Liu %+ College of Communication Engineering, Jilin University, Changchun, People's Republic of China %A Tao Hou %+ College of Communication Engineering, Jilin University, Changchun, People's Republic of China %A Fu Liu %+ College of Communication Engineering, Jilin University, Changchun, People's Republic of China %K UCI datasets %K clustering algorithm %K membership values %K unbalanced dataset %K unbalanced clusters %K iteration methods %K FCM %K fuzzy C-means method %X 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. %@ 0013-5194 %T Improving fuzzy c-means method for unbalanced dataset %B Electronics Letters %D November 2015 %V 51 %N 23 %P 1880-1882 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=7e61s7xmjs7h.x-iet-live-01content/journals/10.1049/el.2015.1541 %G EN