A survey on trajectory clustering analysis
This is an incremental survey paper for researchers in trajectory data mining, covering surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control.
This paper surveys trajectory clustering methods, categorizing them into unsupervised, supervised, and semi-supervised algorithms, and analyzes their limitations in complex conditions like application scenarios and data dimensions.
This paper comprehensively surveys the development of trajectory clustering. Considering the critical role of trajectory data mining in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory clustering has attracted growing attention. Existing trajectory clustering methods can be grouped into three categories: unsupervised, supervised and semi-supervised algorithms. In spite of achieving a certain level of development, trajectory clustering is limited in its success by complex conditions such as application scenarios and data dimensions. This paper provides a holistic understanding and deep insight into trajectory clustering, and presents a comprehensive analysis of representative methods and promising future directions.