%0 Journal Article
%T Research of gene clustering hybrid algorithm based on particle pair and extremal optimization<br>基于粒子对和极值优化的基因聚类混合算法研究
%A XUAN Jun-bo
%A WU Xiao-xia
%A WANG Zhen-zhen
%A ZHANG Chao-ying
%A <br>禤浚波
%A 吴小霞
%A 王珍珍
%A 张超英
%J 计算机应用研究
%D 2011
%I 
%X In order to solve the problem that particle pair algorithm exists local optimization premature to lower precision, this paper suggested a new hybrid algorithm based on particle pair optimization(PPO) and extremal optimization(EO). The hybrid algorithm used the merits of PPO and EO, and assigned the fast cluster result of the K-means to initialize a particle and introduced the extremal optimization algorithm in the iteration process of elitist particle pair according to interval iteration, which could ensure convergence and avoid local optimization premature in the later period, so it improved the precision of the clustering result. Applying the hybrid algorithm to gene expression data, the experiment results indicate that the hybrid algorithm obtains better clustering precision and stability than the K-means algorithm and particle pair algorithm.
%K gene clustering
%K K-means algorithm
%K particle pair
%K extremal optimization algorithm
%K hybrid algorithm<br>基因聚类
%K K-means算法
%K 粒子对
%K 极值优化算法
%K 混合算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=1F8EB868F38CE072B5252E41E352D15F&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=F3090AE9B60B7ED1&sid=B79AD94FEDB43E41&eid=F830EB41032E5CFE&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=9