Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Detecting Commuting Patterns by Clustering Subtrajectories
ISAAC '08 Proceedings of the 19th International Symposium on Algorithms and Computation
FARM: Feature-Assisted Aggregate Route Mining in Trajectory Data
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Approximating the Fréchet distance for realistic curves in near linear time
Proceedings of the twenty-sixth annual symposium on Computational geometry
Mining trajectory corridors using fréchet distance and meshing grids
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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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.