%0 Conference Paper %1 lee-trajectory-clustering-partition-2007 %A Lee, Jae-Gil %A Han, Jiawei %A Whang, Kyu-Young %B Proceedings of the 2007 ACM SIGMOD international conference on Management of data %C New York, NY, USA %D 2007 %I ACM %K clustering dynamic %P 593--604 %R 10.1145/1247480.1247546 %T Trajectory clustering: a partition-and-group framework %U http://www.cs.uiuc.edu/~hanj/pdf/sigmod07_jglee.pdf %X 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. %@ 978-1-59593-686-8

@inproceedings{lee-trajectory-clustering-partition-2007, 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.}, acmid = {1247546}, added-at = {2011-11-02T17:57:15.000+0100}, address = {New York, NY, USA}, author = {Lee, Jae-Gil and Han, Jiawei and Whang, Kyu-Young}, biburl = {https://www.bibsonomy.org/bibtex/241a7b6a683ad0be44b78114cac647153/mhwombat}, booktitle = {Proceedings of the 2007 ACM SIGMOD international conference on Management of data}, description = {Trajectory clustering}, doi = {10.1145/1247480.1247546}, interhash = {fee78bf6593e52756fa1ead6cec0a2e6}, intrahash = {41a7b6a683ad0be44b78114cac647153}, isbn = {978-1-59593-686-8}, keywords = {clustering dynamic}, location = {Beijing, China}, numpages = {12}, pages = {593--604}, publisher = {ACM}, series = {SIGMOD '07}, timestamp = {2016-07-12T19:25:30.000+0200}, title = {Trajectory clustering: a partition-and-group framework}, url = {http://www.cs.uiuc.edu/~hanj/pdf/sigmod07_jglee.pdf}, year = 2007 }