DocumentCode
1588035
Title
K-means clustering analysis based on immune genetic algorithm
Author
Cai, Lecai ; Yao, Xingyan ; He, Zhiyong ; Liang, Xingjian
Author_Institution
Inst. of Comput. Applic., Sichuan Univ. of Sci. & Eng., Zigong, China
fYear
2010
Firstpage
413
Lastpage
418
Abstract
A novel algorithm, the k-means clustering algorithm based on immune genetic algorithm (KMCIGA) is put forward. To improve the Genetic operators, the conception of concentration in the immune algorithm and the dynamic chromosome coding are used. Strategies and methods of selecting vaccines and constructing an immune operator are also given. KMCIGA is illustrated to be obviously better than the traditional K-Means clustering algorithm in clustering division, and can improve the searching ability and adaptability, greatly increase the converging speed.
Keywords
biology computing; genetic algorithms; pattern clustering; KMCIGA; dynamic chromosome coding; immune genetic algorithm; k-means clustering analysis; Algorithm design and analysis; Biological cells; Classification algorithms; Clustering algorithms; Encoding; Gallium; Genetic algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
World Automation Congress (WAC), 2010
Conference_Location
Kobe
ISSN
2154-4824
Print_ISBN
978-1-4244-9673-0
Electronic_ISBN
2154-4824
Type
conf
Filename
5665368
Link To Document