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Mining mobile group patterns: a trajectory-based approach

Published: 18 May 2005 Publication History

Abstract

In this paper, we present a group pattern mining approach to derive the grouping information of mobile device users based on a trajectory model. Group patterns of users are determined by distance threshold and minimum time duration. A trajectory model of user movement is adopted to save storage space and to cope with untracked or disconnected location data. To discover group patterns, we propose ATGP algorithm and TVG-growth that are derived from the Apriori and VG-growth algorithms respectively.

References

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Y.-H. Liu "Mining Mobile Group Patterns: A Trajectory-based Approach," master thesis, Department of Information Management, National Sun Yat-sen U., available at http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730104-102312.
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Cited By

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  • (2019)An Experimental Evaluation of Grouping Definitions for Moving EntitiesProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3347146.3359346(89-98)Online publication date: 5-Nov-2019
  • (2016)Fast fuzzy trajectory clustering strategy based on data summarization and rough approximationCluster Computing10.1007/s10586-016-0603-819:3(1411-1420)Online publication date: 1-Sep-2016
  • (2013)OLAP for moving object dataInternational Journal of Intelligent Information and Database Systems10.1504/IJIIDS.2013.0517457:1(79-112)Online publication date: 1-Jan-2013
  • Show More Cited By

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Information & Contributors

Information

Published In

cover image Guide Proceedings
PAKDD'05: Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
May 2005
860 pages
ISBN:3540260765
  • Editors:
  • Tu Bao Ho,
  • David Cheung,
  • Huan Liu

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 May 2005

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Cited By

View all
  • (2019)An Experimental Evaluation of Grouping Definitions for Moving EntitiesProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3347146.3359346(89-98)Online publication date: 5-Nov-2019
  • (2016)Fast fuzzy trajectory clustering strategy based on data summarization and rough approximationCluster Computing10.1007/s10586-016-0603-819:3(1411-1420)Online publication date: 1-Sep-2016
  • (2013)OLAP for moving object dataInternational Journal of Intelligent Information and Database Systems10.1504/IJIIDS.2013.0517457:1(79-112)Online publication date: 1-Jan-2013
  • (2011)Finding closed MEMOsProceedings of the 23rd international conference on Scientific and statistical database management10.5555/2032397.2032427(369-386)Online publication date: 20-Jul-2011
  • (2010)Discovery of evolving convoysProceedings of the 22nd international conference on Scientific and statistical database management10.5555/1876037.1876057(196-213)Online publication date: 30-Jun-2010
  • (2010)Mining frequent closed patterns in pointset databasesInformation Systems10.1016/j.is.2009.10.00735:3(335-351)Online publication date: 1-May-2010
  • (2009)Mining frequent trajectory patterns in spatial-temporal databasesInformation Sciences: an International Journal10.1016/j.ins.2009.02.016179:13(2218-2231)Online publication date: 1-Jun-2009
  • (2008)OLAP for TrajectoriesProceedings of the 19th international conference on Database and Expert Systems Applications10.1007/978-3-540-85654-2_32(340-347)Online publication date: 1-Sep-2008
  • (2007)Mining temporal co-orientation pattern from spatio-temporal databasesProceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining10.5555/1764441.1764544(895-903)Online publication date: 22-May-2007
  • (2006)On mining 2 step walking pattern from mobile usersProceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I10.1007/11751540_119(1090-1099)Online publication date: 8-May-2006

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