Non-separable Transforms for Clustering Trajectories

  • Alfredo Cuzzocrea
  • Elio Masciari
Conference paper

DOI: 10.1007/978-3-642-23863-5_58

Volume 6882 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Cuzzocrea A., Masciari E. (2011) Non-separable Transforms for Clustering Trajectories. In: König A., Dengel A., Hinkelmann K., Kise K., Howlett R.J., Jain L.C. (eds) Knowlege-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science, vol 6882. Springer, Berlin, Heidelberg

Abstract

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.

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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