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Trajectory clustering: a partition-and-group framework

Trajectory clustering: a partition-and-group framework Trajectory Clustering: A Partition-and-Group Framework — Department of Computer Science University of Illinois at Urbana-Champaign Jae-Gil Lee, Jiawei Han jaegil@uiuc.edu, hanj@cs.uiuc.edu Department of Computer Science / AITrc KAIST Kyu-Young Whang kywhang@cs.kaist.ac.kr ABSTRACT Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the rst phase, we present a formal trajectory partitioning algorithm using the minimum description length (MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Trajectory clustering: a partition-and-group framework

Association for Computing Machinery — Jun 11, 2007

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References (23)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2007 by ACM Inc.
ISBN
978-1-59593-686-8
doi
10.1145/1247480.1247546
Publisher site
See Article on Publisher Site

Abstract

Trajectory Clustering: A Partition-and-Group Framework — Department of Computer Science University of Illinois at Urbana-Champaign Jae-Gil Lee, Jiawei Han jaegil@uiuc.edu, hanj@cs.uiuc.edu Department of Computer Science / AITrc KAIST Kyu-Young Whang kywhang@cs.kaist.ac.kr ABSTRACT Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the rst phase, we present a formal trajectory partitioning algorithm using the minimum description length (MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications

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