摘要
提出了一种基于密度网格的数据流聚类算法。通过引入"隶度",对传统的基于网格密度的数据流聚类算法,以网格内数据点的个数作为网格密度的思想加以改进,解决了一个网格内属于两个类的数据点以及边界点的处理问题。从而既利用了基于网格算法的高效率,还较大程度地提高了聚类精度。
This paper introduced a density grid-based data stream clustering algorithm.Through the introduction of the ＂subject degree＂,the traditional density grid-based clustering algorithm for data stream was improved by taking the data points within the grid as the grid density,thereby resolving the problem of data points belonging to two classes in one grid as well as the treatment of boundary points.Therefore,not only the high efficiency of the grid-based algorithm was utilized,but also the clustering accuracy was raised significantly.
出处
《计算机应用》
CSCD
北大核心
2010年第4期1093-1095,共3页
journal of Computer Applications
关键词
聚类
数据流
网格
滑动窗口
隶度
clustering
data stream
grid
sliding window
subject degree
