Research of novel artificial immune model applied to data analysis
一种面向数据分析的新型人工免疫模型研究

This paper proposed a novel artificial immune network model. By introducing elaborate immune operators, such as clonal selection, mutual cooperation and mutual suppression, the network evolves from single antibody into clusters that can adapt to local distribution and local density of original antigen population. The general framework of learning algorithm was described. Several critical procedures were also analyzed. Experiments based on synthetic dataset were carried out to demonstrate the behavior of learning process and evaluate the performance of the proposed model.