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Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
Trajectory Mining Icdm 2009
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Trajectory Mining Icdm 2009


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  • Morning everyoneNow I would like to give a brief introduction about my previous work, actually it`s my first time to attend this workshop and ICDM.So if you have any questions during my talk, pls feel free to ask meOk, then So our motivation of this work is to analyze ...The result of this research could be apply to …Next slide we will give two picture to explain this motivation more vividly
  • Let us take a look at the left picture , This is a part view of beijing planet map. The red line means the hot tourist path at this coarse level, Of course it dosent come from real data, we draw the line here just want to show our idea.At this level, one centimeter is equal to half a kilometer. Suppose we get the red line by clustering trajectories with some density measure, If we want to look more details of the Forbidden City, we should zoom in as much as is needed.Then we could get the right picture, one centimeter in the picture is equal to two hundred meters, we think the clusters density should be consistent with this grain size , with more low density measure we could get the cluster as this red line
  • The trajectory we mentioned
  • Let us give an illustration of the Frechet distance: a man is walking witha dog on a leash. This man is walking on the one curve, the dog on theother one. Both may vary their speed, but backtracking is not allowed.Then the Frechet distance of the curves is the minimal length of a leashthat is necessary. The Frechet method has the advantage of computing distancesonly on homologous points and not between closest points as for theHausdorff distance
  • The method at this time is not up to do this fast , the problem is to do this in a meaningful way
  • Transcript

    • 1. Multi-granularity Visualization of Trajectory Clusters using Sub-trajectory Clustering
      Cheng Chang, Bao-Yao Zhou
      HP Labs China
    • 2. Motivations
      Analyzing objects movement to identify frequently moving paths at different scale can improve a large set of services such as:
      Traffic management.
      Location-based service.
      Customer behavior analysis.
      CCTV Surveillance.
      15 March 2010
    • 3. Motivations
      15 March 2010
    • 4. How to realize
      Using Trajectory segment as the main interest unit
      Clustering all the Trajectory segment based on density control
      Extract the cluster multi-granularity
      15 March 2010
    • 5. Trajectory definition
      Definition 1.A trajectory is a sequence of multi-dimensional points.
      Definition 2.A cluster is a set of similar sub-trajectories.
      15 March 2010
    • 6. Method
      Select the set of interesting trajectories
      Corner detection
      Segment the trajectories
      Get the augmented order of the sub-trajectories
      Frechet distance measure
      Extract the cluster with different granularity
      15 March 2010
    • 7. Trajectory segmentation
      Scans the point sequence of a trajectory and selects candidate corner points according to the calculation of the open angle
      Remove redundant candidates.
      15 March 2010
    • 8. Distance measure for sub-trajectory clustering
      Fréchet Distance
      The maximal distance between two oriented lines.
      • Examine all possible walks.
      • 9. Yields a set M of maximum leash lengths.
      • 10. dF= shortest leash length in M.
      15 March 2010
    • 11. Distance measure for sub-trajectory clustering
      Fréchet Distance
      Discrete Fréchet Distance
      15 March 2010
    • 12. Density-based clustering
      A cluster: a maximal set of density-connected points
      Discover clusters of arbitrary shape in spatial databases with noise
      OPTICS: ordering points to identify the clustering structure
      It computes an augmented clustering-ordering for automatic cluster analysis.
      Based on this idea, two values need to be introduced:
      Core distance
      Reachability distance
      15 March 2010
    • 13. Density-based clustering
      OPTICS Algorithm
      create an ordering of a database, additionally
      store the core-distance and a suitable reachability-distance for each points.
      Then such information is used to extract all density-based clusters with respect to any distance ε ’ smaller than the generating distance ε from this order.
      15 March 2010
    • 14. Extract sub-trajectory clusters
      Scanning the order and assigning the cluster-membership
      Check the ascription of each sub-trajectory in cluster
      15 March 2010
    • 15. Density-based clustering
      OPTICS Motivation
      input parameters (e.g., Eps) are difficult to be determined
      We let the user to explore the clustering result at different scale
      one global parameter setting may not fit all the clusters
      Through the visualization
      it’s good to allow users to have flexibility in selecting clusters
      15 March 2010
    • 16. Visualization
      15 March 2010
    • 17. Experiments
      The experiments are performed on the animal movement dataset provided by the Starkey project
      15 March 2010
    • 18. Experiments
      15 March 2010
    • 19. Conclusion
      A new framework to discover multi-granularity clusters by clustering sub-trajectories.
      An extension of the OPTIC algorithm for curve segments by using Fréchet distance measurement.
      The user can tune the density to explore the visualization of clustering results at different spatial granularities.
      15 March 2010
    • 20. Thank you for your attention.
      15 March 2010