Unsupervised Trajectory Sampling

  • Nikos Pelekis
  • Ioannis Kopanakis
  • Costas Panagiotakis
  • Yannis Theodoridis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6323)


A novel methodology for efficiently sampling Trajectory Databases (TD) for mobility data mining purposes is presented. In particular, a three-step unsupervised trajectory sampling methodology is proposed, that initially adopts a symbolic vector representation of a trajectory which, using a similarity-based voting technique, is transformed to a continuous function that describes the representativeness of the trajectory in the TD. This vector representation is then relaxed by a merging algorithm, which identifies the maximal representative portions of each trajectory, at the same time preserving the space-time mobility pattern of the trajectory. Finally, a novel sampling algorithm operating on the previous representation is proposed, allowing us to select a subset of a TD in an unsupervised way encapsulating the behavior (in terms of mobility patterns) of the original TD. An experimental evaluation over synthetic and real TD demonstrates the efficiency and effectiveness of our approach.


Trajectory Databases Sampling Symbolic Trajectories 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nikos Pelekis
    • 1
  • Ioannis Kopanakis
    • 2
  • Costas Panagiotakis
    • 3
  • Yannis Theodoridis
    • 4
  1. 1.Dept. of Statistics and Insurance ScienceUniv. of PiraeusGreece
  2. 2.Tech. Educational Inst. of CreteGreece
  3. 3.Dept. of Computer ScienceUniv. of CreteGreece
  4. 4.Dept. of InformaticsUniv. of PiraeusGreece

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