Open AccessImproving fuzzy c ‐means method for unbalanced dataset
Author(s) -
Liu Yun,
Hou Tao,
Liu Fu
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.1541
Subject(s) - cluster analysis , data mining , fuzzy logic , cluster (spacecraft) , fuzzy clustering , computer science , pattern recognition (psychology) , artificial intelligence , mathematics , programming language
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.