DocumentCode
2692349
Title
Memetic Algorithm based fuzzy clustering
Author
Do, Anh-Duc ; Cho, Siu-Yeung
Author_Institution
Nanyang Technol. Univ., Singapore
fYear
2007
fDate
25-28 Sept. 2007
Firstpage
2398
Lastpage
2404
Abstract
This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.
Keywords
fuzzy set theory; pattern clustering; fuzzy c-means clustering algorithm; memetic algorithm; overlapping class boundaries; spread data distributions; Clustering algorithms; Evolutionary computation; Genetic algorithms; Iterative algorithms; Minimization methods; Partitioning algorithms; Robustness; Search methods; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location
Singapore
Print_ISBN
978-1-4244-1339-3
Electronic_ISBN
978-1-4244-1340-9
Type
conf
DOI
10.1109/CEC.2007.4424771
Filename
4424771
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