Trajectory Outlier Detection: A Partition-and-Detect Framework

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dc.contributor.authorLee, Jae-Gil-
dc.contributor.authorHan, Jiawei-
dc.contributor.authorLi, Xiaolei-
dc.date.accessioned2013-03-27T23:28:39Z-
dc.date.available2013-03-27T23:28:39Z-
dc.date.created2012-02-06-
dc.date.issued2008-04-07-
dc.identifier.citationInt'l Conf. on Data Engineering (IEEE ICDE), v., no., pp.140 - 149-
dc.identifier.urihttp://hdl.handle.net/10203/162231-
dc.description.abstractOutlier 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 that TRAOD correctly detects outlying sub-trajectories from real trajectory data.-
dc.languageENG-
dc.titleTrajectory Outlier Detection: A Partition-and-Detect Framework-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.beginningpage140-
dc.citation.endingpage149-
dc.citation.publicationnameInt'l Conf. on Data Engineering (IEEE ICDE)-
dc.identifier.conferencecountryMexico-
dc.identifier.conferencecountryMexico-
dc.contributor.localauthorLee, Jae-Gil-
dc.contributor.nonIdAuthorHan, Jiawei-
dc.contributor.nonIdAuthorLi, Xiaolei-
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IE-Conference Papers(학술회의논문)
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