摘要
在很多有效的聚类算法中,DBSCAN算法对于聚类空间数据有着非常好的性能,依赖于基于密度的聚类定义,DBSCAN可以发现任意形状的聚类,而且执行效率很高。但是,DBSCAN没有考虑非空间属性,而非空间属性对聚类的结果也起着十分重要的作用。在DBSCAN的基础上,参考DBRS的概念,进一步考虑了非空间属性的数据类型,从而提出了可以处理空间和非空间数据的新的聚类方法,并给出了主要的算法。
In many effective algorithms for cluster, DBSCAN algorithm is outstanding for its good performance in spatial data。Relying on a density-based notion of clusters, DBSCAN can discover clusters of arbitrary shape. But it cant support non-spatial attributes, in some application of cluster, the non-spatial attributes play important role. Based on the DBSCAN, referencing some notion of DBRS and considering data type of non-spatial attribute, the paper proposed a method of extension of DBSCAN and gave main algorithm。The algorithm can operate spatial and non-spatial attribute.
出处
《计算机应用》
CSCD
北大核心
2005年第6期1379-1381,共3页
journal of Computer Applications
关键词
空间数据挖掘
空间聚类
非空间属性
密度
spatial data mining
spatial cluster
non-Spatial attribute
density
