进化数据流中基于密度的聚类算法  

Density-based Clustering Algorithm in Evolving Data Stream

作　　者：蔡春丽[1] 王惠玲[1] 孙延明[1] 

机构地区：[1]中国电子科技集团公司第四十九研究所,哈尔滨150001

出　　处：《计算机工程》2009年第9期57-59,共3页Computer Engineering

摘　　要：分析当前数据流聚类算法的优点及不足,提出一种新的进化数据流中基于密度的聚类算法——Sdstream算法,该算法能够分析并处理大规模进化数据流,利用真实数据集和仿真数据集对其进行性能测试,实验结果表明,该算法具有良好的适用性、有效性和可扩展性,能够取得较高的聚类效果。On basis of analyzing the advantages and weaknesses of the current clustering algorithm of data streams, this paper introduces a new density-based clustering algorithm in evolving data streams SDStream, which can analyze and deal with large-scale evolving data stream. Its performance is tested by using both real datasets and synthetic datasets. Experimental results show this algorithm has better perpformance of applicability, effectiveness and extension and achieves a higher quality of clustering.

关 键 词：数据挖掘 进化数据流 聚类 滑动窗口 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]