邓敏, 刘启亮, 李光强, 肖奇. 一种基于似最小生成树的空间聚类算法[J]. 武汉大学学报 ( 信息科学版), 2010, 35(11): 1360-1364.
引用本文: 邓敏, 刘启亮, 李光强, 肖奇. 一种基于似最小生成树的空间聚类算法[J]. 武汉大学学报 ( 信息科学版), 2010, 35(11): 1360-1364.
DENG Min, LIU Qiliang, LI Guangqiang, XIAO Qi. A Spatial Clustering Algorithm Based on Minimum Spanning Tree-like[J]. Geomatics and Information Science of Wuhan University, 2010, 35(11): 1360-1364.
Citation: DENG Min, LIU Qiliang, LI Guangqiang, XIAO Qi. A Spatial Clustering Algorithm Based on Minimum Spanning Tree-like[J]. Geomatics and Information Science of Wuhan University, 2010, 35(11): 1360-1364.

一种基于似最小生成树的空间聚类算法

A Spatial Clustering Algorithm Based on Minimum Spanning Tree-like

  • 摘要: 根据空间邻近目标的距离变化情况,定义了边长变化因子概念,给出了一种似最小生成树的构建方法。在此基础上,提出了一种基于似最小生成树的空间聚类算法。模拟数据和实际数据分析发现,基于似最小生成树的空间算法能够发现任意形状的空间簇和异常点,并能够很好地适应空间数据分布不均匀的特点。通过与经典的DBSCAN算法比较,发现基于似最小生成树的空间聚类算法比DBSCAN算法更具有实用性。

     

    Abstract: The concept of edge variation factor is firstly defined based upon the distance variation among the entities in the spatial neighborhood.An approach is presented to construct the minimum spanning tree-like(MST-L for short).Further,a MST-L based spatial clustering algorithm(MSTLSC for short) is developed.Two tests are implemented to demonstrate that the MSTLSC algorithm very robust and suitable to find the clusters with arbitrary shape,especially the algorithm has good adaptive characteristic.A comparative test is made to prove the MSTLSC algorithm better than classic DBSCAN algorithm.

     

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