Improved kernel-based fuzzy clustering algorithm

Abstract

Since the traditional kernel fuzzy clustering algorithm does not take account of the level of contribution the every feature makes toward clustering,and it also has a shortcoming of easy falling into a situation of a local optimum,an improved kernel fuzzy clustering algorithm was put forward.It combined the advantage of the global optimum to construct a simple and effective fitness function that can avoid plunging local optimum.This improved algorithm gave every feature a weighted coefficient,in which the ReliefF algorithm was used to assign the weights for every feature.Compared with the traditional algorithm,this one has made some significant progress,and the experimental result has proved its effectiveness.