• Save
Trajectory Mining Icdm 2009
Upcoming SlideShare
Loading in...5

Trajectory Mining Icdm 2009







Total Views
Views on SlideShare
Embed Views



3 Embeds 5

http://www.linkedin.com 3
http://www.lmodules.com 1
http://www.slideshare.net 1



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment
  • 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

Trajectory Mining Icdm 2009 Trajectory Mining Icdm 2009 Presentation Transcript

  • Multi-granularity Visualization of Trajectory Clusters using Sub-trajectory Clustering
    Cheng Chang, Bao-Yao Zhou
    HP Labs China
  • 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
  • Motivations
    15 March 2010
  • 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
  • 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
  • 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
  • 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
  • Distance measure for sub-trajectory clustering
    Fréchet Distance
    The maximal distance between two oriented lines.
    • Examine all possible walks.
    • Yields a set M of maximum leash lengths.
    • dF= shortest leash length in M.
    15 March 2010
  • Distance measure for sub-trajectory clustering
    Fréchet Distance
    Discrete Fréchet Distance
    15 March 2010
  • 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
  • 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
  • Extract sub-trajectory clusters
    Scanning the order and assigning the cluster-membership
    Check the ascription of each sub-trajectory in cluster
    15 March 2010
  • 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
  • Visualization
    15 March 2010
  • Experiments
    The experiments are performed on the animal movement dataset provided by the Starkey project
    15 March 2010
  • Experiments
    15 March 2010
  • 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
  • Thank you for your attention.
    15 March 2010