An Adaptive Polyclonal Clustering Algorithm and Its Convergence Analysis
MA Li1,2, JIAO LiCheng1, BAI Lin2, CHEN ChangGuo3
1.Intelligent Information Processing Institute, Xidian University, Xi'an 7100712. Information Center, Xi'an Institute of Post and Telecommunications, Xi'an 7100613. Intervideo Digital Science and Technology Inc., Xi'an, 710075
Abstract：Based on a simple description of the basic principle of biology immune and clonal process, a polyclonal clustering algorithm with selfadaptive feature is put forward. The main idea of the algorithm is to put various operators in artificial immune system into clustering process and adjust clustering numbers automatically by affinity function. The recombination operator is introduced to increase the diversity of antibody group so as to broaden the search scope of the global optimization solution and avoid early mature phenomenon of the group. And the nonconsistent mutation operator is introduced to enhance the adaptability and optimize the performance of local solution seeking, meanwhile convergence of the algorithm is speeded up. The experimental result shows that reasonable clustering could be realized by the proposed algorithm.
[1] Zhuo Guangyan. Principles of Immunology. Shanghai, China: Shanghai Scientific and Technological Publishers, 1998 (in Chinese) (周光炎.免疫学原理.上海:上海科学技术出版社, 1998) [2] Jerne N K. Towards a Network Theory of the Immune System. Annual Immunology, 1974, 125(C): 373389 [3] Jiao Licheng, Du Haifeng. Development and Prospect of the Artificial Immune System. Acta Electronica Sinica, 2003, 31(10): 15401549 (in Chinese) (焦李成,杜海峰.人工免疫系统进展与展望.电子学报, 2003, 31(10): 15401549) [4] Dasgupta D. Artificial Neural Networks and Artificial Immune Systems: Similarities and Differences // Proc of the IEEE International Conference on Computational Cybernetics and Simulation. Orlando, USA, 1997, Ⅱ: 873878 [5] de Castro L N, von Zuben F J. An Evolutionary Immune Network for Data Clustering // Proc of the 6th Brazilian Symposium on Neural Networks. Oakland, USA, 2000: 8489 [6] Li Jie, Gao Xinbo, Jiao Licheng. A CAS Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values. Acta Electronica Sinica, 2004, 32(3): 357362 (in Chinese) (李 洁,高新波,焦李成.一种基于CSA的混合属性特征大数据集聚类算法.电子学报, 2004, 32(3): 357362) [7] Gibson D, Kleinberg J M, Raghavan P. Clustering Categorical Data: An Approach Based on Dynamical Systems // Gupta A, Shmueli O, Widom J, eds. Proc of the 24th International Conference on Very Large Data Bases. New York, USA, 1998: 311322 [8] Guha S, Rastogi R, Shim K. CURE: An Efficient Clustering Algorithm for Large Databases // Haas L M, Tiwary A, eds. Proc of the ACM SIGMOD International Conference on Management of Data. Seattle, USA, 1998: 7384 [9] Zhang Wenxiu, Liang Yi. Mathematical Foundation of Genetic Algorithms. Xi'an, China: Xi'an Jiaotong University Press, 1999 (in Chinese) (张文修,梁 怡.遗传算法的数学基础.西安:西安交通大学出版社, 1999) [10] Zhou Zhihua, Cao Cungen. Neural Networks and It's Applications. Beijing, China: Tsinghua University Press, 2004 (in Chinese) (周志华,曹存根.神经网络及其应用.北京:清华大学出版社, 2004) [11] Wang Lei. Immune Evolutionary Theory and It's Applications. Ph.D Dissertation. Xi'an, China: Xidian University. School of Electronic Engineering, 2001: 3539 (in Chinese) (王 磊.免疫进化计算理论及应用.博士学位论文. 西安:西安电子科技大学.电子工程学院, 2001: 3539) [12] Sheng Zhou, Xie Shiqian, Pan Chengyi. Probability Theory and Mathematical Statistics. Beijing, China: Higher Education Press, 1989 (in Chinese) (盛 骤,谢式千,潘承毅.概率论与数理统计.北京:高等教育出版社, 1989) [13] Jiao Licheng, Liu Fang. Intelligent Data Mining and Knowledge Discovery. Xi'an, China: Xidian University Press, 2006 (in Chinese) (焦李成,刘 芳.智能数据挖掘与知识发现.西安:西安电子科技大学出版社, 2006)
2008, Vol. 21 
