Multiscale Frequent Co-movement Pattern Mining

Thanks to recent prevalence of location tracking technologies, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about the behavior of moving objects such as people, animals, and vehicles. In particular, mining patterns from "co-movements" of objects (such as movements by players of a sports team, joints of the human body while walking, and vehicles in a transportation network) can lead to the discovery of interesting patterns (e.g., offense tactics of a sports team, gait signature of a person, and driving behaviors causing heavy traffic). Various trajectory mining and frequent pattern mining techniques have been proposed to discover patterns in trajectory datasets and more generally, event sequences. However, existing approaches are inapplicable for co-movement pattern mining from multi-trajectory datasets. In this paper, we propose a novel and efficient framework for co-movement pattern mining. We also extend this framework for efficient mining of such patterns at multiple spatial scales. The performance of the proposed solutions is evaluated by conducting extensive experiments using two real datasets, a soccer game dataset and a human gait dataset. Our experimental results show that our proposed algorithms are promising.