Study in neighbor artificial immune net work based on kernel clustering
基于核聚类的近邻人工免疫网络算法研究

To solve the issue of high time complexity and algorithm structure of aiNet, this paper proposed a modified aiNet algorithm:KN-aiNet. Based on the structure of neighbor aiNet, the algorithm took antibody data as core, then clustered data around the core, used energy level as affinity. The algorithm redefined the creation of antibody, took region growing to match distance, took kernel function to improve clustering effect and reduce time complexity. The simulation proves the clustering accuracy of KN-aiNet improves 11. 5% compared with aiNet, 4. 56% compared with neighbor aiNet, and time complexity declines 0. 503 s compared with aiNet, 0. 823 s compared with neighbor aiNet.