%0 Journal Article
%T Study in neighbor artificial immune net work based on kernel clustering<br>基于核聚类的近邻人工免疫网络算法研究
%A LI Xin
%A LI Li-jun
%A GAO Zi-cheng
%A <br>李　昕
%A 李立君
%A 高自成
%J 计算机应用研究
%D 2012
%I 
%X 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.
%K aiNet
%K neighbor aiNet
%K kernel clustering
%K region growing<br>人工免疫网络
%K 近邻aiNet
%K 核函数
%K 区域生长
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=3178011684088478762493DFC6D09868&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=DF92D298D3FF1E6E&sid=8C5000880BA36CC7&eid=9468524DE79F8441&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=12