基于时空模式的轨迹数据聚类算法  

Trajectory data clustering algorithm based on spatio-temporal pattern

作　　者：石陆魁[1,2] 张延茹 张欣[1] 

机构地区：[1]河北工业大学计算机科学与软件学院,天津300401 [2]河北省大数据计算重点实验室(河北工业大学),天津300401

出　　处：《计算机应用》2017年第3期854-859,895,共7页journal of Computer Applications

基　　金：天津市应用基础与前沿技术研究计划项目(14JCZDJC31600);河北省自然科学基金专项(F2016202144);河北省高等学校科学技术研究项目(ZD2014030)~~

摘　　要：针对轨迹聚类算法在相似性度量中多以空间特征为度量标准,缺少对时间特征的度量,提出了一种基于时空模式的轨迹数据聚类算法。该算法以划分再聚类框架为基础,首先利用曲线边缘检测方法提取轨迹特征点;然后根据轨迹特征点对轨迹进行子轨迹段划分;最后根据子轨迹段间时空相似性,采用基于密度的聚类算法进行聚类。实验结果表明,使用所提算法提取的轨迹特征点在保证特征点具有较好简约性的前提下较为准确地描述了轨迹结构,同时基于时空特征的相似性度量因同时兼顾了轨迹的空间与时间特征,得到了更好的聚类结果。Because the existing trajectory clustering algorithms in the similarity measurement usually used the spatial characteristics as the standards the characteristics lacking the consideration of temporal, a trajectory data clustering algorithm based on spatial-temporal pattern was proposed. The proposed algorithm was based on partition-and-group framework. Firstly, the trajectory feature points were extracted by using the curve edge detection method. Then the sub-trajectory segments were divided according to the trajectory feature points. Finally, the clustering algorithm based on density was used according to the spatio-temporal similarity between sub-trajectory segments. The experimental results show that the trajectory feature points extracted using the proposed algorithm are more accurate to describe the trajectory structure under the premise that the feature points have better simplicity. At the same time, the similarity measurement based on spatio-temporal feature obtains better clustering result by taking into account both spatial and temporal characteristics of trajectory.

关 键 词：时空模式 轨迹数据 曲线边缘检测 相似性度量 密度聚类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]