Fast and Accurate Trajectory Streams Clustering

  • Elio Masciari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6809)


Trajectory data streams are huge amounts of data pertaining to time and position of moving objects. They are continuously generated by different sources exploiting 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 this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data streams poses interesting challenges both for their proper definition and acquisition, thus making the mining process harder than for classical point data. 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.


Discrete Fourier Transform Supply Chain Management Trajectory Data Scalable Video Density Base Cluster 
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.


  1. 1.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD 1996 (1996)Google Scholar
  2. 2.
    Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: KDD 2007, pp. 330–339 (2007)Google Scholar
  3. 3.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)zbMATHGoogle Scholar
  4. 4.
    Lee, J.G., Han, J., Li, X., Gonzalez, H.: TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. PVLDB 1(1) (2008)Google Scholar
  5. 5.
    Li, Z., Lee, J.-G., Li, X., Han, J.: Incremental clustering for trajectories. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5982, pp. 32–46. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Lloyd, S.: Least squares quantization in pcm. IEEE TOIT 28 (1982)Google Scholar
  7. 7.
    Press, W.H., et al.: Numerical Recipes in C++. Cambridge University Press, Cambridge (2001)Google Scholar
  8. 8.
    Taubman, D., Secker, A.: Lifting-based invertible motion adaptive transform (limat) framework for highly scalable video compression. IEEE Transactions on Image Processing 12(12), 1530–1542 (2003)Google Scholar
  9. 9.
    Wang, W., Yang, J., Muntz, R.R.: Sting: A statistical information grid approach to spatial data mining. In: VLDB 1997, pp. 186–195 (1997)Google Scholar
  10. 10.
    Zhang, T., Ramakrishnan, R., Livny, M.: Birch: An efficient data clustering method for very large databases. In: SIGMOD 1996, pp. 103–114 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Elio Masciari
    • 1
  1. 1.ICAR-CNRItaly

Personalised recommendations