摘要
CURE算法是针对大规模数据聚类算法的典型代表。提出了一种新的算法K-CURE,该方法基于划分思想对CURE算法作了改进,同时给出了在聚类中剔除孤立点的时机选择方法。测试表明,改进后的算法效率明显高于原算法,且聚类效果良好。
CURE is a typical clustering algorithm that is introduced in this article to improve the CURE based on occasion of eliminating outlier during clustering.Experiments designed for the mining of mass data.A new algorithm K-CURE is partition.A method is also described to explain how to choose the indicate that the improved algorithm does improve the CURE in both efficiency and effectiveness.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第31期175-177,共3页
Computer Engineering and Applications
基金
江苏省计算机信息处理技术重点实验室开放基金(No.JSK0604)
关键词
数据挖掘
层次聚类
代表对象
CURE
孤立点
data mining
hierarchical clustering
representative objects
CURE
outlier
