Liao L, Patterson D J, Fox D, et al. Learning and inferring transportation routines[J]. Artificial Intelligence, 2007,171(5):311-331.
[2]
Yuan J, Zheng Y, Zhang C, et al. T-drive: Driving directions based on taxi trajectories[C]. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2010:99-108.
[3]
Giannotti F, Nanni M, Pinelli F, et al. Trajectory pattern mining[C]. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2007:330-339.
[4]
Yu Y, Cao L, Rundensteiner E A, et al. Detecting moving object outliers in massive-scale trajectory streams[C]. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014:422-431.
[5]
Li X, Li Z, Han J, et al. Temporal outlier detection in vehicle traffic data[C]. In: Proceedings of the 25th International Conference on Data Engineering. Washington: IEEE Computer Society, 2009:1319-1322.
[6]
Ge Y, Xiong H, Liu C, et al. A taxi driving fraud detection system[C]. 2011 IEEE 11th International Conference on Data Mining (ICDM), 2011:181-190.
[7]
Barnett V, Lewis T. Outliers in statistical data[M]. New York:Wiley, 1994.
[8]
Knorr E M, Ng R T. Finding intentional knowledge of distance-based outliers[C]. In: Proceedings of the 25th International Conference on Very Large Data Bases. Edinburgh, Scotland: Springer-Verlag, 1999,99:211-222.
[9]
Breunig M M, Kriegel H P, Ng R T, et al. LOF: Identifying density-based local outliers[C]. In: Proceedings of 2000 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2000,29(2):93-104.
[10]
Aggarwal C C, Yu P S. Outlier detection for high dimensional data[C]. In: Proceedings of 2001 ACM SIGMOD International Conference on Management of Data. California: ACM, 2001,30(2):37-46.
[11]
姜金凤.移动对象轨道异常检测算法的研究[D].南京:南 京航空航天大学,2010:1-19.
[12]
Knorr E M, Ng R T, Tucakov V. Distance-based outliers: algorithms and applications[J]. The VLDB Journal？The International Journal on Very Large Data Bases, 2000,8(3-4):237-253.
[13]
Li X, Han J, Kim S, et al. ROAM: Rule-and motif-based anomaly detection in massive moving object data sets[C]. In: Proceedings of the SIAM International Conference on Data Mining. Minneapolis. Minnesota: SIAM, 2007,7: 273-284.
Lee J G, Han J, Li X. Trajectory outlier detection: A partition-and-detect framework[C]. In: Proceedings of the 24th International Conference on Data Engineering. Washington: IEEE Computer Society, 2008:140-149.
[16]
Liu L, Fan J, Qiao S, et al. Efficiently mining outliers from trajectories of unrestraint movement[C]. In: Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering, 2010,2:V2-261-V2-265.
[17]
Isaksson C, Dunham M H. A comparative study of outlier detection algorithms[M]. In: Machine Learning and Data Mining in Pattern Recognition. Berlin: Springer Berlin Heidelberg, 2009:440-453.
[18]
Armbrust M, Fox A, Griffith R, et al. A view of cloud computing[J]. Communications of the ACM, 2010,53(4): 50-58.
[19]
Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters[J]. Communications of the ACM, 2008,51(1):107-113.
[20]
Dean J, Ghemawat S. MapReduce: A flexible data processing tool[J]. Communications of the ACM, 2010,53(1): 72-77.
[21]
Lee J G, Han J, Whang K Y. Trajectory clustering: A partition-and-group framework[C]. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, 2007:593-604.
[22]
UNISYS. The Atlantic hurricane database[EB/OL]. [2014-9-10]. http://weather.unisys.com/hurricane/atlantic/.
