基于轨迹划分与密度聚类的移动用户重要地点识别方法  

Important Location Identification of Mobile Users Based on Trajectory Division and Density Clustering Method

作　　者：杨震[1] 王红军 YANG Zhen;WANG Hong-jun(Electronic Countermeasures College,National University of Defense Technology,Hefei 230037,China)

机构地区：[1]国防科技大学电子对抗学院

出　　处：《计算机科学》2019年第8期23-27,共5页Computer Science

基　　金：国家自然科学基金(61273302)资助

摘　　要：移动用户轨迹数据作为新兴的空间轨迹数据,可用于分析个体或群体的行为特征、兴趣爱好,在智慧城市、交通规划和反恐维稳等领域应用广泛。为了从庞大的数据集中识别出移动用户的重要地点,提出了一种基于转角偏移度与距离偏移量的轨迹划分算法。该算法首先通过轨迹划分提取出用户的重要地点候选集,然后采用一种改进的密度聚类算法进一步对用户的候选重要地点实现聚类,从而识别出用户的最终重要地点。在Geolife轨迹数据集与Foursquare用户签到数据集上的实验表明,采用轨迹划分与密度聚类相结合的重要地点识别方法具有比现有的重要地点识别方法更高的准确率,证明了所提方法的可行性与优越性。As emerging spatial trajectory data,mobile user trajectory data can be used to analyze individual or group behavioral characteristics,hobbies and interests,and are widely used in smart cities,transportation planning,and anti-terrorism maintenance.In order to identify the important locations of mobile user from a huge data set,this paper proposed a trajectory division method based on the angle and distance offset.The method firstly extracts the important locations candidate set by trajectory division,and then further clusters the important locations through an improved density clustering algorithm,extracting the final important location of user.The experiment on Geolife trajectory data set and Foursquare data set shows that the important location identification method combining trajectory division and density clustering has higher accuracy than other existing important location identification method,which proves the feasibility and superiority of the proposed method.

关 键 词：移动用户 轨迹划分 重要地点 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]