Immunodomaince based Clonal Selection Clustering Algorithm

Authors:
Ruochen Liu;Xiangrong Zhang;Neng Yang;Qifeng Lei;Licheng Jiao
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, P.O. Box 224, No. 2 South Taibai Road, Xi'an 710071, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, P.O. Box 224, No. 2 South Taibai Road, Xi'an 710071, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, P.O. Box 224, No. 2 South Taibai Road, Xi'an 710071, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, P.O. Box 224, No. 2 South Taibai Road, Xi'an 710071, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, P.O. Box 224, No. 2 South Taibai Road, Xi'an 710071, PR China
Venue:
Applied Soft Computing
Year:
2012

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Abstract

Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. Firstly, by introducing a new immunodomaince operator to Clonal Selection Algorithm (CSA), the gene of elites in antibody population can be extracted and generalized to ordinary antibodies so as to gain on-line priori knowledge and share information among individuals. Then, one iteration of Fuzzy C-means clustering algorithm (FCM) and adaptive updating mechanism of antibody population are utilized to improve the diversity of antibody population in order to speed up the convergence speed. The proposed method has been extensively compared with FCM, GA-clustering algorithm (GACA) and Clonal Selection Algorithm based FCM (CSAFCM) over a test suit of several real life data sets and synthetic data sets. Experimental results indicate the superiority of the ICSCA over FCM, GAFCM and CSAFCM on clustering accuracy and robustness.