A Dynamic Immune Algorithm with Immune Network for Data Clustering

摘要：

This paper proposes a Dynamic Immune algorithm used for data clustering analysis. Its immune mechanism, partially inspired by self-organized mapping theory, is introduced to adjust the antibodys quantity and improve clustering quality. In order to guarantee clustering quality for highly non-linear distributed inputs, Kernel method is adopted to increase the clustering quality. In order to enhance direct descriptions about the clusterings center and result in input space, a new distance dimension instead of Euclidean distance is introduced by adopting Kernel substitution method while the training procedure is still running in input space. Simulation results are also provided to verify the algorithms feasibility, clustering performance and anti-noise capability.

作者: Lei Wu Lei Peng

作者单位: School of Applied Mathematics University of Electronic Science and Technology of China Chengdu, Sich School of Computer Science and Engineering University of Electronic Science and Technology of China

会议类型: 国际会议

会议名称: 2007年通信、电路与系统国际会议(2007 International Conference on Communications,Circuits and Systems Proceedings)

会议地点: 日本福冈

会议语种:英文

在线出版日期: 2007-07-11（万方平台首次上网日期，不代表论文的发表时间）