A review of moving object trajectory clustering algorithms

作者：Yuan, Guan^*; Sun, Penghui; Zhao, Jie; Li, Daxing; Wang, Canwei

来源：Artificial Intelligence Review, 2017, 47(1): 123-144.

DOI：10.1007/s10462-016-9477-7

• Trajectory clustering
• Moving objects
• Similarity measure
• Moving pattern
• Data mining

Clustering is an efficient way to group data into different classes on basis of the internal and previously unknown schemes inherent of the data. With the development of the location based positioning devices, more and more moving objects are traced and their trajectories are recorded. Therefore, moving object trajectory clustering undoubtedly becomes the focus of the study in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object clustering and analyze typical moving object clustering algorithms presented in recent years. In this paper, we firstly summarize the strategies and implement processes of classical moving object clustering algorithms. Secondly, the measures which can determine the similarity/dissimilarity between two trajectories are discussed. Thirdly, the validation criteria are analyzed for evaluating the performance and efficiency of clustering algorithms. Finally, some application scenarios are point out for the potential application in future. It is hope that this research will serve as the steppingstone for those interested in advancing moving object mining.

• 出版日期2017-1
• 单位中国矿业大学（北京）