摘要: 以整条轨迹为目标的聚类方法存在轨迹较长的问题。为此,提出一种以轨迹子段为聚类目标的聚类算法CTIHD。给出一种新的轨迹子段距离度量方法,用以消除轨迹子段之间的公共偏差。利用特征点概念将轨迹划分成轨迹子段集,计算轨迹子段之间的相似度,由此实现聚类。实验结果表明,该算法相比同类算法具有更好的轨迹聚类效果。
关键词:
轨迹聚类,
运动模式,
Hausdorff距离,
点特征矩阵,
轨迹子段
Abstract: For problems which the whole trajectory as the target for the clustering, this paper proposes a clustering algorithm called CTIHD(Clustering of Trajectories based on Improved Hausdorff Distance), which uses a sub- trajectory as the target for the clustering. In this algorithm, in order to effectively calculate the similarity between the trajectory, the algorithm defines a new sub-trajectory distance metrics, the definition can not only effectively eliminate the public error between sub-trajectory, but also take full account of sub-trajectory contains the movement feature. In algorithm, trajectory is divided into sub-trajectories uses the concept of the trajectory of feature point, It uses the proposed the definition of trajectory distance metrics between sub-trajectories to calculated similarity between sub-trajectories; On this basis, the use of traditional clustering methods for sub-trajectory clustering. Experimental results show that the algorithm can achieve better trajectory clustering effect than the existing methods.
Key words:
trajectory clustering,
movement pattern,
Hausdorff Distance(HD),
point characteristic matrix,
sub-trajectory
中图分类号:
陈锦阳, 宋加涛, 刘良旭, 王让定. 基于改进Hausdorff距离的轨迹聚类算法[J]. 计算机工程, 2012, 38(17): 157-161.
CHEN Jin-Yang, SONG Jia-Chao, LIU Liang-Xu, WANG Rang-Ding. Trajectory Clustering Algorithm Based on Improved Hausdorff Distance[J]. Computer Engineering, 2012, 38(17): 157-161.