  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.
