Trajectory Outlier Detection: A Partition-and-Detect Framework

  • Authors:
  • Jae-Gil Lee;Jiawei Han;Xiaolei Li

  • Affiliations:
  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. jaegil@uiuc.edu;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. hanj@uiuc.edu;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. xli10@uiuc.edu

  • Venue:
  • ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
  • Year:
  • 2008

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Abstract

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 that TRAOD correctly detects outlying sub-trajectories from real trajectory data.