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 :
بازگشت