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. © 2013 Springer-Verlag.
CITATION STYLE
Aung, H. H., Guo, L., & Tan, K. L. (2013). Mining sub-trajectory cliques to find frequent routes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8098 LNCS, pp. 92–109). https://doi.org/10.1007/978-3-642-40235-7_6
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