%0 Journal Article
%T Data stream clustering algorithm based on probability density<br>一种基于概率密度的数据流聚类算法
%A ZHANG Wei
%A CHEN Chun-yan
%A <br>陈春燕
%A 张伟
%J 计算机应用
%D 2007
%I 
%X Data stream is characterized by infinite data and quick stream speed, so traditional clustering algorithm cannot be applied to data stream clustering directly, In view of above questions, a probability-density-based data stream clustering algorithm was proposed. It requires only newly arrived data, not the entire historical data, to be saved in memory. It applies EM algorithm on the newly arrived data and updates probability-density function by incremental Gaussian mixture model. Experimental results show that the algorithm is very effective to solve data stream clustering.
%K data stream
%K clustering
%K Gaussian mixture model
%K probability-density<br>数据流
%K 聚类
%K 高斯混合模型
%K 概率密度
%K 概率密度函数
%K 数据流
%K 聚类算法
%K probability
%K density
%K based
%K clustering
%K algorithm
%K stream
%K 实验
%K 增量式
%K 高斯混合模型
%K 利用
%K 历史
%K 存储
%K 方法
%K 聚类问题
%K 应用
%K 流速
%K 无限
%K 数据量
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=99CDA9A92912D561B6C625D092D4512A&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=E158A972A605785F&sid=E348995F86F60FD3&eid=899CC9158FC43EF4&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=9