[1] HOMAYOUNFAR A, HO A T S, ZHU N, et al. Multi-vehicle convoy analysis based on ANPR data[C]//Proceedings of the 4th International Conference on Imaging for Crime Detection and Prevention. Piscataway, NJ:IEEE, 2011:38. [2] ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Menlo Park, CA:AAAI Press, 1996:226-231. [3] GUDMUNDSSON J, VAN KREVELD M. Computing longest duration flocks in trajectory data[C]//Proceedings of Annual ACM International Symposium on Advances in Geographic Information Systems. New York:ACM, 2006:35-42. [4] JEUNG H, YIU M L, ZHOU X, et al. Discovery of convoys in trajectory databases[J]. Proceedings of the VLDB Endowment, 2008, 1(1):1068-1080. [5] LI Z, DING B, HAN J, et al. Swarm:mining relaxed temporal moving object clusters[J]. Proceedings of the VLDB Endowment, 2010, 3(1/2):723-734. [6] TANG L, ZHENG Y, YUAN J, et al. A framework of traveling companion discovery on trajectory data streams[J]. ACM Transactions on Intelligent Systems and Technology, 2014, 5(1):1-34. [7] DEZA E, DEZA M M. Encyclopedia of Distances[M]. Berlin:Springer, 2009:94. [8] AGRAWAL R, FALOUTSOS C, SWAMI A. Efficient similarity search in sequence databases[C]//Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms. Berlin:Springer, 1993:69-84. [9] YI B K, JAGADISH H V, FALOUTSOS C. Efficient retrieval of similar time sequences under time warping[C]//Proceedings of the 14th International Conference on Data Engineering. Piscataway, NJ:IEEE, 1998:201-208. [10] VLACHOS M, KOLLIOS G, GUNOPULOS D. Discovering similar multidimensional trajectories[C]//Proceedings of the 18th International Conference on Data Engineering. Piscataway, NJ:IEEE, 2002:673-684. [11] CHEN L, ÖZSU M T, ORIA V. Robust and fast similarity search for moving object trajectories[C]//Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. New York:ACM, 2005:491-502. [12] CHEN L, NG R. On the marriage of Lp-norms and edit distance[C]//Proceedings of the 13th International Conference on Very Large Data Bases. Toronto:VLDB Endowment, 2004, 30:792-803. [13] JEUNG H, YIU M L, JENSEN C S. Trajectory Pattern Mining[M]. New York:Springer, 2011:143-177. [14] LEE J G, HAN J, WHANG K Y. Trajectory clustering:a partition-and-group framework[C]//Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data. New York:ACM, 2007:593-604. [15] 方艾芬, 李先通, 蔄世明,等. 基于关联规则挖掘的伴随车辆发现算法[J]. 计算机应用与软件, 2012, 29(2):94-96.(FANG A F,LI X T,MAN S M,et al.A discovery algorithm of traveling companions based on association rule mining[J]. Computer Applications and Software,2012,29(2):94-96.) [16] HAN J, PEI J, YIN Y. Mining frequent patterns without candidate generation[J].ACM SIGMOD Record, 2000, 29(2):1-12. [17] 曹波, 韩燕波, 王桂玲. 基于车牌识别大数据的伴随车辆组发现方法[J]. 计算机应用, 2015, 35(11):3203-3207.(CAO B, HAN Y B, WANG G L. Discovery method of travelling companions based on big data of license plate recognition[J]. Journal of Computer Applications, 2015, 35(11):3203-3207.) [18] 朱美玲, 王雄斌, 张守利, 等. 基于大规模流式车牌识别数据的即时伴随车辆发现[J]. 中国科学技术大学学报, 2016, 46(1):47-55.(ZHU M L, WANG X B, ZHANG S L, et al. Instant traveling companion discovery based on large scale streaming ANPR data[J]. Journal of University of Science and Technology of China, 2016, 46(1):47-55.) |