Mining Sub-trajectory Cliques to Find Frequent Routes

Knowledge of the routes frequently used by the tracked objects is embedded in the massive trajectory databases. Such knowledge has various applications in optimizing ports' operations and route-recommendation systems but is difficult to extract especially when the underlying road network information is unavailable. 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 NP-Complete, we proposed two approximate algorithms based on the Apriori algorithm. Empirical results showed that our algorithms can run fast and their results are intuitive.