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Sequential pattern mining from trajectory data
2013
Proceedings of the 17th International Database Engineering & Applications Symposium on - IDEAS '13
In this paper, we study the problem of mining for frequent trajectories, which is crucial in many application scenarios, such as vehicle traffic management, hand-off in cellular networks, supply chain management. We approach this problem as that of mining for frequent sequential patterns. Our approach consists of a partitioning strategy for incoming streams of trajectories in order to reduce the trajectory size and represent trajectories as strings. We mine frequent trajectories using a sliding
doi:10.1145/2513591.2513653
dblp:conf/ideas/MasciariGZ13
fatcat:fouwnt72ozdhvpll56hl6si2pm