Discovering closed frequent patterns in moving trajectory database

The increasing availability of tracking devices bring larger amounts of trajectories representing people's moving location histories. In this paper, we aimed to mine closed frequent patterns in moving trajectory database. Such closed frequent patterns can help us to understand general mobile behaviors in compact representation. In this work, we first presented a conception of spatiotemporal region of interesting (STROI) to capture the attribute of moving trajectory in spatial and temporal dimensions. Second, based on the set of STROIs distributing in given geospatial region, we transformed trajectory data into STROI element sequence data at different time slice with respect to corresponding STROIs. Third, we modified the closed sequence pattern mining algorithm CloSpan to adapt to closed moving trajectory pattern discovery. Finally, the approaches are then validated by a range of synthetic data sets to evaluate the usefulness and efficiency.