作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2011, Vol. 37 ›› Issue (14): 56-58. doi: 10.3969/j.issn.1000-3428.2011.14.017

• 软件技术与数据库 • 上一篇    下一篇

基于聚类的空间数据多比例尺索引树

邹志文,费洪哲,李 根   

  1. (江苏大学计算机学院,江苏 镇江 212013)
  • 收稿日期:2010-12-17 出版日期:2011-07-20 发布日期:2011-07-20
  • 作者简介:邹志文(1968-),男,副教授,主研方向:空间数据库;费洪哲、李 根,硕士研究生
  • 基金资助:
    国家自然科学基金资助项目(60773049);江苏省研究生科研创新计划基金资助项目(CX07B_125z);江苏省中小企业技术创新计划基金资助项目(BC2008140);镇江市社会发展计划基金资助项目(SH2008028)

Multi-scale Index Tree of Spatial Data Based on Clustering

ZOU Zhi-wen, FEI Hong-zhe, LI Gen   

  1. (College of Computer, Jiangsu University, Zhenjiang 212013, China)
  • Received:2010-12-17 Online:2011-07-20 Published:2011-07-20

摘要: 针对现有空间对象多尺度索引结构聚簇性不高的问题,在R树索引的基础上提出一种基于聚类的空间数据多比例尺索引结构。利用树的层次结构反映空间数据的多比例尺特性,用k-means算法对相同等级的空间对象进行聚类分组,减少空间区域覆盖和重叠。实验结果表明,该方法与基于四叉树的多比例尺索引相比,能有效提高空间数据多比例尺显示的性能。

关键词: 多比例尺, R树, 聚类算法, 空间索引, 地理信息系统

Abstract: Due to the problem about clustering of multi-scale index structure for the existing space object is not high, this paper represents a cluster-based multi-scale spatial data indexing structure based on R-tree index, which uses hierarchical tree to reflect the multi-scale features of spatial data, and uses k-means algorithm to cluster groups on the same level of spatial objects, reducing coverage and overlapping regions of space. Experiments result shows that compared with other ways, the algorithm has a distinct superiority in the speed of multi-scale display of spatial data.

Key words: multi-scale, R-tree, clustering algorithm, spatial index, Graphic Information System(GIS)

中图分类号: