Behavior Grouping based on Trajectory Mining

  • Shusaku Tsumoto
  • Shoji Hirano
Conference paper


Human movements in a limited space may have similar characteristicsif their targets are the same as others. This paper focuses on such a nature of human movements as a trajectory in two or three dimensional spaces and proposes a method for grouping trajectories as two-dimensional time-series data. Experimental results show that this method successfully captures the structural similarity between trajectories.


Human Movement Dissimilarity Matrix Replacement Cost Segment Pair Contiguous Segment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    N. Ueda and S. Suzuki: A Matching Algorithm of Deformed Planar Curves Using Multiscale Convex/Concave Structures. IEICE Transactions on Information and Systems, J73-D-II(7)992–1000 (1990).Google Scholar
  2. 2.
    X. Wang, A. Wirth, and L. Wang: Structure-Based Statistical Features and Multivariate Time Series Clustering. Proceedings of the 7th IEEE International Conference on Data Mining, 351–360 (2007).Google Scholar
  3. 3.
    M. Vlachos, G. Kollios and D. Gunopulos: Discovering similar multidimensional trajectories, Proceedings of the IEEE 18th International Conference on Data Engineering, 673–684 (2002).Google Scholar
  4. 4.
    J-G. Lee, J. Han, and K-Y Whang: Trajectory clustering: a partition-and-group framework. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, 593–604 (2007).Google Scholar
  5. 5.
    A. P. Witkin: Scale-space filtering. Proc. the Eighth IJCAI, 1019–1022 (1983).Google Scholar
  6. 6.
    F. Mokhtarian and A. K. Mackworth: Scale-based Description and Recognition of planar Curves and Two Dimensional Shapes (1986). IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(1)24–43.CrossRefGoogle Scholar
  7. 7.
    G. Dudek and J. K. TostsosShape Representation and Recognition from Multiscale Curvature. Comp. Vis. Img Understanding, 68(2):170–189 (1997).CrossRefGoogle Scholar
  8. 8.
    J. Babaud and A. P. Witkin and M. Baudin and O. DudaUniqueness of the Gaussian kernel for scale-space filtering (1986). IEEE Trans. PAMI, 8(1):26–33.MATHGoogle Scholar
  9. 9.
    T. LindebergScale-Space for Discrete Signals. IEEE Transactions on Pattern Analysis and Machine Intellivence, 12(3):234–254 (1990).CrossRefGoogle Scholar
  10. 10.
    Lowe, D.GOrganization of Smooth Image Curves at Multiple Scales. International Journal of Computer Vision, 3:119–130 (1980).CrossRefGoogle Scholar
  11. 11.
  12. 12.
    E. Keogh and M. Pazzani: Scaling up dynamic time warping for datamining applications. Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 285–289 (2000).Google Scholar
  13. 13.
    B. S. Everitt, S. Landau, and M. Leese: Cluster Analysis Fourth Edition. Arnold Publishers (2001).Google Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Shimane UniversityIzumoJapan

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