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一种基于似最小生成树的空间聚类算法 被引量：8

A Spatial Clustering Algorithm Based on Minimum Spanning Tree-like
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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。 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.
作者 邓敏 刘启亮 李光强 肖奇
机构地区 中南大学测绘与国土信息工程系
出处 《武汉大学学报（信息科学版）》 EI CSCD 北大核心 2010年第11期1360-1364,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(200805) 江苏省资源环境信息工程重点实验室开放研究基金资助项目(20080101) 中南大学研究生论文创新选题资助项目(713360010)
关键词 空间聚类 自适应 边长变化因子 似最小生成树 spatial clustering adaptive edge variation factor minimum spanning tree-like
分类号 P208 [天文地球—地图制图学与地理信息工程]
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参考文献14

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  • 2李斐,王殊伟,柯宝贵.应用聚类分析方法进行实测重力数据的选点优化[J].武汉大学学报（信息科学版）,2009,34(3):257-260. 被引量：6
  • 3王海军,张德礼.基于空间聚类的城镇土地定级方法研究[J].武汉大学学报（信息科学版）,2006,31(7):628-631. 被引量：25
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二级参考文献77

共引文献276

同被引文献76

  • 1张惟皎,刘春煌,李芳玉.聚类质量的评价方法[J].计算机工程,2005,31(20):10-12. 被引量：61
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  • 6Zhang T, Ramakrishnan R, Livny M. BIRCH,. an Efficient Data Clustering Method for very Large Da- tabases[C]. The ACM SIGMOD International Con- ference on Management of Data, Montreal, Canada, 1996.
  • 7Guha S, Rastogi R, Shim K. CURE: an Efficient Clustering Algorithm for Large Databases[C]. The 1998 ACM-SIGMOD International Conference on Management of Data, Seattle, Washington D C, 1998.
  • 8Ester M, Kriegel H P, Sander J, et al. A Density- Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]. The 2nd the In- ternational Conference on Knowledge Discovery and Data Mining, Portland, OR, 1996.
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  • 10Estivill-Castro V, Lee I. Multi-level Clustering and Its Visualization for Exploratory Spatial Analysis [J]. GeoInformatiea, 2002, 6(2): 123-152.

引证文献8

二级引证文献33

武汉大学学报（信息科学版）
2010年 第11期

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