Mining regular behaviors based on multidimensional trajectories

Abstract

In the information fusion domain, mining regular behaviors is very important to task classification, anomaly behavior detection, situation assessment and threat estimation. Through clustering multidimensional trajectories which are accumulated in all kinds of electronic information systems, regular behaviors could be mined. Most of the trajectories clustering methods are clustering spatial position closed trajectories to a cluster. They cannot distinguish behaviors whose space position is similar but the moving speed and direction are quite different. Some sub-trajectory clustering methods which presented similarity measures considering segment direction, speed, and angle could solve this problem, but they are not suitable to some application scenarios which should clustering the whole trajectories. In this paper, we proposed a multidimensional trajectory clustering algorithm to mine regular behaviors by considering the attribute, type, position, velocity and course characteristics, and implement it on two experiments. This research is very helpful for mining all the regular behaviors in different application scenarios and would have a wide prospect in expert and intelligent systems.