摘要
提出基于密度的网格聚类算法GDCA,发现大规模空间数据库中任意形状的聚类.该算法首先将数据空间划分成若干体积相同的单元,然后对单元进行聚类.只有密度不小于给定阈值的单元才得到扩展,从而大大降低了时间复杂性.在GDCA的基础上,给出增量式聚类算法IGDCA,适用于数据的批量更新.
Although many clustering algorithms have been proposed so far, seldom was focused on high-dimensional and incremental databases. This paper introduces a grid density-based clustering algorithm——GDCA, which discovers clusters with arbitrary shape in spatial databases. It first partitions the data space into a number of units, and then deals with units instead of points. Only those units with the density no less than a given minimum density threshold are useful in extending clusters. An incremental clustering algorithm——IGDCA is also presented, applicable in periodically incremental environment.
出处
《软件学报》
EI
CSCD
北大核心
2002年第1期1-7,共7页
Journal of Software
基金
国家自然科学基金
国家重点基础研究发展规划973资助项目~~
