%0 Journal Article
%T Semi-supervised learning based on K-means clustering algorithm<br>基于半监督学习的K-均值聚类算法研究
%A LIU Tao
%A YIN Hong-jian
%A <br>刘涛
%A 尹红健
%J 计算机应用研究
%D 2010
%I 
%X This paper constructed a new classified function which mixed Euclidean distance with supervising information. Taking into account that K-means algorithm was sensitive to the initial center, used search space of particle swarm algorithm was used to simulate the clustering Euclidean space to find a better cluster center of clustering. At the same time, brought up a strategy of species dynamic management to improve the efficiency of particle swarm optimization search. The algorithm got a good clustering accuracy on a number of UCI testing data sets.
%K semi-supervised clustering
%K improved K-means algorithm
%K species particle swarm optimization based on the dynamic management<br>半监督聚类
%K 改进的K-均值算法
%K 动态管理种群的粒子群算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=70EF4D842EDD56DFC675C324EB6FB2B6&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=38B194292C032A66&sid=8637B749179B02B3&eid=DE4E739E935BD9A7&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=2&reference_num=8