Trajectory Clustering: A Partition-and-group Framework

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 <i>sub</i>-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 <i>partition-and-group</i> 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 <i>sub</i>-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm <i>TRACLUS</i>. Our algorithm consists of two phases: <i>partitioning</i> and <i>grouping</i>. 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.