Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-40235-7_6
Title: Mining sub-trajectory cliques to find frequent routes
Authors: Aung, H.H.
Guo, L.
Tan, K.-L. 
Issue Date: 2013
Citation: Aung, H.H.,Guo, L.,Tan, K.-L. (2013). Mining sub-trajectory cliques to find frequent routes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8098 LNCS : 92-109. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40235-7_6
Abstract: 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.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/78232
ISBN: 9783642402340
ISSN: 03029743
DOI: 10.1007/978-3-642-40235-7_6
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