摘要
现实世界当中的各种约束条件限制了空间聚类必须考虑这些限制条件的存在。主要研究带障碍物的空间聚类,采用K-中心点算法进行聚类分析,在解决空间对象绕过障碍物的最短距离时引进改进的郭涛算法进行求解,对于中小规模数据体现了较高的执行效率。通过理论分析和实验验证,该算法是可行的。
In the real-world, constraints limits the spatial clustering must take into account the conditions of these restrictions, this paper studied the spatial clustering with obstacles. It mainly used the K-medoid algorithm to cluster, and it introduced an improved algorithm Guo Tao to solve the distance of spatial objects in the presence of obstacles. It is higher efficiency for small and medium-sized data. Through theoretical analysis and experimental, the algorithm is feasible.
出处
《计算机科学》
CSCD
北大核心
2009年第12期197-198,222,共3页
Computer Science
关键词
空间聚类
障碍约束
演化算法
Spatial clustering, Obstacle constrain, Evolutionary algorithm
