测绘学报 ›› 2015, Vol. 44 ›› Issue (10): 1152-1159.doi: 10.11947/j.AGCS.2015.20150136

• 地图学与地理信息 • 上一篇    下一篇

综合线面特征分布的点目标多尺度聚类方法

余莉, 甘淑, 袁希平, 杨明龙   

  1. 昆明理工大学国土资源工程学院, 云南 昆明 650093
  • 收稿日期:2015-03-13 修回日期:2015-07-07 出版日期:2015-10-20 发布日期:2015-10-23
  • 作者简介:余莉(1987—),女,博士生,主要研究方向为空间数据挖掘、建模与分析.E-mail:woshiyuli@126.com
  • 基金资助:
    国家自然科学基金(41261092;71163023;41161061)

Multi-scale Clustering of Points Synthetically Considering Lines and Polygons Distribution

YU Li, GAN Shu, YUAN Xiping, YANG Minglong   

  1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
  • Received:2015-03-13 Revised:2015-07-07 Online:2015-10-20 Published:2015-10-23
  • Supported by:
    The National Science Foundation of China(Nos.41261092,71163023,41161061)

摘要: 考虑空间数据分布的复杂性与不连续性,提出了一种点目标聚类方法.算法利用全要素Voronoi图准确识别与表达点目标与线面实体的空间相关性;根据点目标位置分布特征计算面积阈值来控制聚类的粒度,同时以空间尺度变化下面积阈值的恒定作为判断尺度收敛的条件,实现点目标的多尺度划分,时间复杂度为O(n log n).经试验验证,聚类尺度随点目标分布特征自适应收敛,算法无须自定义参数,能够有效地发现受线面目标约束的任意形态点目标集群,对异常值处理稳健.

关键词: 空间聚类, 多尺度, 全要素Voronoi图, 约束

Abstract: Considering the complexity and discontinuity of spatial data distribution, a clustering algorithm of points was proposed. To accurately identify and express the spatial correlation among points,lines and polygons, a Voronoi diagram that is generated by all spatial features is introduced. According to the distribution characteristics of point's position, an area threshold used to control clustering granularity was calculated. Meanwhile, judging scale convergence by constant area threshold, the algorithm classifies spatial features based on multi-scale, with an O(n log n) running time.Results indicate that spatial scale converges self-adaptively according with distribution of points.Without the custom parameters, the algorithm capable to discover arbitrary shape clusters which be bound by lines and polygons, and is robust for outliers.

Key words: spatial clustering, multi-scale, Voronoi diagram of all features, constraints

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