Mining Sub-trajectory Cliques to Find Frequent Routes

Knowledge of the routes frequently used by the tracked objects are embedded in the massive trajectory databases archiving spatialtemporal movement data of the objects in question. Such knowledge has various applications in optimizing ports’ operations, understanding wild-life behaviours, and navigation/route-recommendation systems but is difficult to extract in many real-life scenarios, where the underlying road network information is not available. We propose a novel approach, which discovers frequent routes without any prior knowledge of the underlying road network by mining sub-trajectory cliques. Since mining all sub-trajectory cliques is an NP-Complete problem, we proposed two approximate algorithms based on the Apriori algorithm. Empirical results showed that our algorithms can run faster than the existing approximation algorithm appeared in [1] and provide a tighter results. Our experiments also showed that the frequent routes reported by our algorithms