Trajectory Clustering: A Partition-and-Group Framework

Lee, Jae-Gil; Han, Jiawei; Whang, Kyu-Young

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https://hdl.handle.net/2142/11301 Copy

Description

Title

Trajectory Clustering: A Partition-and-Group Framework

Author(s)

• Lee, Jae-Gil
• Han, Jiawei
• Whang, Kyu-Young

Issue Date

2007-03

Keyword(s)

computer science

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 first 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.

Type of Resource

text

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http://hdl.handle.net/2142/11301

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