Non-separable Transforms for Clustering Trajectories

  • Alfredo Cuzzocrea
  • Elio Masciari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6882)


Trajectory data refer to time and position of moving objects generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). Mining such amounts of data is challenging, since the possibility to extract useful information from these peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks and supply chain management. In this paper, we address the problem of trajectory data streams clustering, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose a complete framework starting from data preparation task that allows us to make the mining step quite effective. Since the validation of data mining approaches has to be experimental we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.


Supply Chain Management Multiresolution Analysis Polynomial Algebra Trajectory Data Lift Scheme 
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 2011

Authors and Affiliations

  • Alfredo Cuzzocrea
    • 1
  • Elio Masciari
    • 1
  1. 1.ICAR-CNR – Institute of Italian National Research CouncilItaly

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