Polyclonal clustering algorithm and its convergence  

作　　者：MA Li JIAO Li-cheng BAI Lin CHEN Chang-guo 

机构地区：[1]InteUigent Information Proceeing Institute, Xidian University, Xi'an 710071, China [2]Information Center, Xi'an Institute of Posts and Telecommunications, Xi'an 710061, China [3]Intervideo Digital Science and Technology Inc., Xi'an 710075, China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2008年第3期110-117,共8页中国邮电高校学报（英文版）

基　　金：National Natural Science Foundation of China (60372050, 60133010);the Hi-Tech Research and Development Program of China (2002AA135080)

摘　　要：Being characteristic of non-teacher learning, self-organization, memory, and noise resistance, the artificial immune system is a research focus in the field of intelligent information processing. Based on the basic principles of organism immune and clonal selection, this article presents a polyclonal clustering algorithm characteristic of self-adaptation. According to the core idea of the algorithm, various immune operators in the artificial immune system are employed in the clustering process; moreover, clustering numbers are adjusted in accordance with the affinity function. Introduction of the recombination operator can effectively enhance the diversity of the individual antibody in a generation population, so that the searching scope for solutions is enlarged and the premature phenomenon of the algorithm is avoided. Besides, introduction of the inconsistent mutation operator enhances the adaptability and optimizes the performance of local solution seeking. Meanwhile, the convergence of the algorithm is accelerated. In addition, the article also proves the convergence of the algorithm by employing the Markov chain. Results of the data simulation experiment show that the algorithm is capable of obtaining reasonable and effective cluster.

关 键 词：clonal selection clustering analysis Markov chain nonconsistence mutation CONVERGENCE 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]