摘要
DBSCAN算法是一种基于密度的空间数据聚类方法,聚类速度快,且能够有效处理噪声点和发现任意形状的空间聚类.但是数据量大时要求较大的内存支持和IO消耗,当空间聚类的密度不均匀,聚类间距离相差很大时,聚类质量较差.本文在DBSCAN算法的基础上提出一个划分不同密度分别聚类的算法.测试结果表明可以改善聚类效果.
DBSCAN is adensity based clustering algorithm that can efficiently discover clusters of arbitrary shape and can effectively handle noise. However, it requires large volume of memory support and needs a lot of IO costs when dealing with large-scale data bases. Furthermore, clustering quality will degrade when the cluster density and the distance between clusters are not even. In this paper, an improved DBSCAN algorithm is presented on the basis of data partitioning. Experimental results show that the new algorithm is snperior to the original DBSCAN in efficiency.
出处
《漳州师范学院学报（自然科学版）》
2009年第1期22-25,共4页
Journal of ZhangZhou Teachers College（Natural Science)
