A CLUSTERING-BASED APPROACH FOR DISCOVERING INTERESTING PLACES IN A SINGLE TRAJECTORY
With the development of many location sensors such as GPS technology and mobile communication devices, a lot of trajectories of users and moving objects can be obtained. These trajectories may contain many interesting individual patterns of the users and moving objects. This creates an appropriate basis for developing efficient new methods for mining moving objects. Semantic clustering of trajectories left behind moving objects is an important aspect in spatio-temporal data mining. Algorithm CB SMo T (Clustering-Based Stops and Moves of Trajectories) is based on a traditional algorithm DBSCAN which is a classical density based clustering approach. There is an important parameter Eps in the algorithm CB-SMoT, whose value can dramatically affect the quality of clustering. With in-depth analysis of spatial and temporal characteristics of trajectory data and some related statistical theory, the trajectory data is preprocessed. A new method of calculating the Eps value is proposed The experiment proves that using this method to calculate the parameter values can significantly improve the quality of clustering.
spatio-temporal clustering clustering trajectories left behind moving objects CB-SMoT algorithm Eps-linear neighborhood
ZHAO Xiu- li XU Wei-xiang
School of Traffic and Transportation Beijing Jiaotong University Beijing, China School of Economics School of Traffic and Transportation Beijing Jiaotong University Beijing, China
国际会议
长沙
英文
2355-2358
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）