DocumentCode
1631093
Title
Fuzzy Clustering by Differential Evolution
Author
Kao, Yucheng ; Lin, Jin-Cherng ; Huang, Shin-Chia
Author_Institution
Dept. of Inf. Manage., Tatung Univ., Taipei
Volume
1
fYear
2008
Firstpage
246
Lastpage
250
Abstract
A fuzzy clustering algorithm based on differential evolution (FCDE) is presented in this paper in order to overcome the disadvantages of traditional fuzzy c-means algorithm (FCM). FCM is sensitive to initialization so that its search is easy to fall into a local optimum. The algorithm we proposed in this paper will avoid this problem and lead to global optimum. The experiments show that FCDE has better performance than FCM and is more efficient particularly when the number of dimension of data becomes large.
Keywords
evolutionary computation; fuzzy set theory; pattern clustering; differential evolution; fuzzy c-means algorithm; fuzzy clustering; Application software; Clustering algorithms; Clustering methods; Computer science; Data mining; Design engineering; Fuzzy systems; Genetic algorithms; Information management; Intelligent systems; Data Clustering; Differential Evolution; Fuzzy c-Means;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on
Conference_Location
Kaohsiung
Print_ISBN
978-0-7695-3382-7
Type
conf
DOI
10.1109/ISDA.2008.270
Filename
4696211
Link To Document