摘要
为了使得空间聚类分析更加适应实际情况,发展了一种同时顾及空间障碍约束与空间位置邻近的空间聚类方法。该方法采用Delaunay三角网描述实体间的邻近关系,并且不依赖用户指定参数。实验验证了本方法的有效性与优越性。
Spatial clustering has been a major research field in spatial data mining;it aims to discover some useful patterns or outliers in a spatial database.In practice,spatial obstacles,as river or mountains should be fully considered in the process of spatial clustering.On that account,a novel spatial clustering method considering spatial obstacles is proposed in this paper.Delaunay triangulation is employed to model spatial proximate relations among entities,and the method can automatically discover clusters with complex structures without user-specified parameters.Experiments on both simulated database and real-world database are utilized to demonstrate the effectiveness and advantage of our method.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2012年第1期96-100,共5页
Geomatics and Information Science of Wuhan University
基金
国家863计划资助项目(2009AA12Z206)
地理空间信息工程国家测绘局重点实验室开放研究基金资助项目(200916201015)
中南大学前沿研究计划资助项目(2010QYZD002)
