TY - GEN
T1 - Trajectory outlier detection
T2 - 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
AU - Lee, Jae Gil
AU - Han, Jiawei
AU - Li, Xiaolei
PY - 2008
Y1 - 2008
N2 - Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub-trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate thatTRAOD correctly detects outlying sub-trajectories from real trajectory data.
AB - Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub-trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate thatTRAOD correctly detects outlying sub-trajectories from real trajectory data.
UR - http://www.scopus.com/inward/record.url?scp=52649161757&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=52649161757&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2008.4497422
DO - 10.1109/ICDE.2008.4497422
M3 - Conference contribution
AN - SCOPUS:52649161757
SN - 9781424418374
T3 - Proceedings - International Conference on Data Engineering
SP - 140
EP - 149
BT - Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Y2 - 7 April 2008 through 12 April 2008
ER -