A Framework for Trajectory Clustering

  • Elio Masciari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5659)


The increasing availability of huge amounts of “thin” data, i.e. data pertaining to time and positions generated by different sources with a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatio-temporal data collections. Mining such amounts of data is challenging, since the possibility of extracting useful information from this particular type 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 issue of clustering spatial trajectories. In the context of trajectory data, this problem is even more challenging than in classical transactional relationships, as here we deal with data (trajectories) in which the order of items is relevant. We propose a novel approach based on a suitable regioning strategy and an efficient clustering technique based on edit distance. Experiments performed on real world datasets have confirmed the efficiency and effectiveness of the proposed techniques.


Supply Chain Management Edit Distance Trajectory Data Subspace Cluster Levenshtein Distance 
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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  1. 1.
    Agrawal, J., Diao, Y., Gyllstrom, D., Immerman, N.: Efficient pattern matching over event streams. In: SIGMOD Conference, pp. 147–160 (2008)Google Scholar
  2. 2.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient Similarity Search in Sequence Databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  3. 3.
    Damerau, F.J.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171–176 (1964)CrossRefGoogle Scholar
  4. 4.
    Agrawal, R., et al.: Automatic subspace clustering of high dimensional data for data mining applications. In: SIGMOD (1998)Google Scholar
  5. 5.
    Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: KDD, pp. 330–339 (2007)Google Scholar
  6. 6.
    Goldin, D., Kanellakis, P.: On similarity queries for time-series data: Constraint specification and implementation. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, pp. 137–153. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  7. 7.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)zbMATHGoogle Scholar
  8. 8.
    Jae-Gil, L., Jiawei, H., Kyu-Young, W.: Trajectory clustering: a partition-and-group framework. In: SIGMOD 2007: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, pp. 593–604. ACM, New York (2007)Google Scholar
  9. 9.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. PVLDB 1(1), 1068–1080 (2008)Google Scholar
  10. 10.
    Jolliffe, I.T.: Principal Component Analysis. Springer Series in Statistics (2002)Google Scholar
  11. 11.
    Kéri, G., Kisvölcsey, Á.: On computing the hamming distance. Acta Cybernetica 16(3), 443–449 (2004)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Lee, J.-G., Han, J., Li, X.: Trajectory outlier detection: A partition-and-detect framework. In: ICDE, pp. 140–149 (2008)Google Scholar
  13. 13.
    Lee, J.-G., Han, J., Li, X., Gonzalez, H.: TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. PVLDB 1(1), 1081–1094 (2008)Google Scholar
  14. 14.
    Levenshtein: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10 (1966)Google Scholar
  15. 15.
    Li, Y., Han, J., Yang, J.: Clustering moving objects. In: KDD, pp. 617–622 (2004)Google Scholar
  16. 16.
    Liu, Y., Chen, L., Pei, J., Chen, Q., Zhao, Y.: Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. In: PerCom., pp. 37–46 (2007)Google Scholar
  17. 17.
    Rafiei, D., Mendelzon, A.: Efficient retrieval of similar time series. In: Procs. 5th Int. Conf. of Foundations of Data Organization (FODO 1998) (1998)Google Scholar
  18. 18.
    Sadri, R., Zaniolo, C., Zarkesh, A.M., Adibi, J.: Expressing and optimizing sequence queries in database systems. ACM Trans. Database Syst. 29(2), 282–318 (2004)CrossRefGoogle Scholar
  19. 19.
    Sharma, A., Paliwal, K.K.: Fast principal component analysis using fixed-point algorithm. Pattern Recognition Letters 28(10), 1151–1155 (2007)CrossRefGoogle Scholar
  20. 20.
    Yang, J., Hu, M.: Trajpattern: Mining sequential patterns from imprecise trajectories of mobile objects. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 664–681. Springer, Heidelberg (2006)CrossRefGoogle Scholar

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© 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 CouncilItaly

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