Trajectory Clustering via Effective Partitioning

  • Elio Masciari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5822)


The increasing availability of huge amounts of data pertaining to time and positions generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatial data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. In this paper, we address the problem of clustering spatial trajectories. In the context of trajectory data, clustering is really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose a novel approach based on a suitable regioning strategy and an efficient and effective clustering technique based on a proper metric. Finally, we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.


Discrete Fourier Transform Supply Chain Management Trajectory Data Mining Sequential Pattern Region Encode 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Elio Masciari
    • 1
  1. 1.ICAR-CNR, Institute for the High Performance Computing of Italian National Research Council 

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