Trajectory outlier detection: A partition-and-detect framework

Jae Gil Lee, Jiawei Han, Xiaolei Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 thatTRAOD correctly detects outlying sub-trajectories from real trajectory data.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Pages140-149
Number of pages10
DOIs
StatePublished - 2008
Event2008 IEEE 24th International Conference on Data Engineering, ICDE'08 - Cancun, Mexico
Duration: Apr 7 2008Apr 12 2008

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other2008 IEEE 24th International Conference on Data Engineering, ICDE'08
CountryMexico
CityCancun
Period4/7/084/12/08

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Fingerprint Dive into the research topics of 'Trajectory outlier detection: A partition-and-detect framework'. Together they form a unique fingerprint.

Cite this