计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (10): 65-68.DOI: 10.3778/j.issn.1002-8331.2009.10.020

• 研究、探讨 • 上一篇    下一篇

移动对象子轨迹段分割与聚类算法

张延玲1,2,刘金鹏2,姜保庆1,2   

  1. 1.河南大学 数据与知识工程研究所,河南 开封 475004
    2.河南大学 计算机与信息工程学院,河南 开封 475004
  • 收稿日期:2008-09-12 修回日期:2008-11-18 出版日期:2009-04-01 发布日期:2009-04-01
  • 通讯作者: 张延玲

Partition and clustering for sub-trajectories of moving objects

ZHANG Yan-ling1,2,LIU Jin-peng2,JIANG Bao-qing1,2   

  1. 1.Institute of Data & Knowledge Engineering,Henan University,Kaifeng,Henan 475004,China
    2.Institute of Computer and Information Engineering,Henan University,Kaifeng,Henan 475004,China
  • Received:2008-09-12 Revised:2008-11-18 Online:2009-04-01 Published:2009-04-01
  • Contact: ZHANG Yan-ling

摘要: 将运动轨迹作为整体聚类会丢失相似子轨迹段,而相似子轨迹段在实际应用中用处很大,如天气预报、交通控制等。提出一种新方法T-CLUS进行轨迹聚类,先将长轨迹分割成许多较短的直子段,再产生子段的增广聚类顺序,最后根据可达性图识别子轨迹聚类结构,得到子轨迹运动模式。实验结果表明T-CLUS方法在从轨迹数据库中发现相似子轨迹上是可伸缩的和正确的。

Abstract: Clustering trajectories as a whole can miss common sub-trajectories.Discovering common sub-trajectories is very useful in many applications,such as weather forecast and traffic control.A new method T-CLUS is proposed to clustering trajectories,which partitions a trajectory into line segments,and then the augmented cluster-ordering of the line segments is generated,finally cluster structure of sub-trajectories is identified by means of reachability plot.Experimental results demonstrate that T-CLUS is scalable and accurate to discover common sub-trajectories from a trajectory database.