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: 

• TP312