Trajectory Clustering via Effective Partitioning

  • Elio Masciari
Conference paper

DOI: 10.1007/978-3-642-04957-6_31

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5822)
Cite this paper as:
Masciari E. (2009) Trajectory Clustering via Effective Partitioning. In: Andreasen T., Yager R.R., Bulskov H., Christiansen H., Larsen H.L. (eds) Flexible Query Answering Systems. FQAS 2009. Lecture Notes in Computer Science, vol 5822. Springer, Berlin, Heidelberg


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.


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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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