Journal of Intelligent Information Systems

, Volume 27, Issue 3, pp 267–289 | Cite as

Time-focused clustering of trajectories of moving objects

  • Mirco Nanni
  • Dino Pedreschi


Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future, due to both technological and social/commercial reasons. From the data mining viewpoint, spatio-temporal trajectory data introduce new dimensions and, correspondingly, novel issues in performing the analysis tasks. In this paper, we consider the clustering problem applied to the trajectory data domain. In particular, we propose an adaptation of a density-based clustering algorithm to trajectory data based on a simple notion of distance between trajectories. Then, a set of experiments on synthesized data is performed in order to test the algorithm and to compare it with other standard clustering approaches. Finally, a new approach to the trajectory clustering problem, called temporal focussing, is sketched, having the aim of exploiting the intrinsic semantics of the temporal dimension to improve the quality of trajectory clustering.


Spatio-temporal data mining Trajectory clustering OPTICS 


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  1. Agrawal, R., Lin, K.-I., Sawhney, H. S., & Shim, K. (1995). Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In VLDB, Zurich, Switzerland pp. 490–501.Google Scholar
  2. Ankerst, M., Breunig, M., Kriegel, H. -P., & Sander, J. (1999). Optics: Ordering points to identify the clustering structure. In Proceedings of ACM SIGMOD international conference on management of data (SIGMOD’99) Philadelphia, Pennsylvania. New York: ACM.Google Scholar
  3. Chomicki, J., & Revesz, P. (1999). Constraint-based interoperability of spatiotemporal databases, GeoInformatica, 3(3), 211–243.CrossRefGoogle Scholar
  4. Chudova, D., Gaffney, S., Mjolsness, E., & Smyth, P. (2003). Translation-invariant mixture models for curve clustering. In Proceedings of ACM SIGKDD KDD ’03, Washington, District of Columbia (pp. 79–88). New York: ACM.CrossRefGoogle Scholar
  5. Ciaccia, P., Patella, M., & Zezula, P. (1997). M-tree: An efficient access method for similarity search in metric spaces. In VLDB’97, Athens, Greece (pp. 426–435). San Francisco, California: Morgan Kaufmann.Google Scholar
  6. Ester, M., Kriegel, H. -P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Second international conference on knowledge discovery and data mining, Portland, Oregon (pp. 226–231) California: AAAI.Google Scholar
  7. Faloutsos, C., & Lin, K. -I. (1995). Fastmap: A fast algorithm for indexing of traditional and multimedia databases. In SIGMOD Conference, San Jose, California (pp. 163–174). New York: ACM.Google Scholar
  8. Gaffney, S., & Smyth, P. (1999). Trajectory clustering with mixture of regression models. In KDD conference, San Diego, California (pp. 63–72). New York: ACM.Google Scholar
  9. Giannotti, F., Mazzoni, A., Puntoni, S., & Renso, C. (2005). Synthetic generation of cellular network positioning data. Technical report, ISTI-CNR.Google Scholar
  10. Gudmundsson, J., van Kreveld, M. J., & Speckmann, B. (2004). Efficient detection of motion patterns in spatio-temporal data sets. In GIS, Washington, District of Columbia (pp. 250–257).Google Scholar
  11. Hadjieleftheriou, M., Kollios, G., Gunopulos, D., & Tsotras, V. J. (2003). On-line discovery of dense areas in spatio-temporal databases. In Proceedings of SSTD’03, Santorini Island, Greece.Google Scholar
  12. Hwang, S.-Y., Liu, Y.-H., Chiu, J.-K., & Lim, E.-P. (2005). Mining mobile group patterns: A trajectory-based approach. In Proceedings of PAKDD’05, Hanoi, Vietnam (pp. 713–718).Google Scholar
  13. Iyengar, V. S. (2004). On detecting space–time clusters. In KDD , Seattle, Washington (pp. 587–592).Google Scholar
  14. Kalpakis, K., Gada, D., & Puttagunta, V. (2001). Distance measures for effective clustering of arima time-series. In ICDM, San Jose, California (pp. 273–280).Google Scholar
  15. Ketterlin, A. (1997). Clustering sequences of complex objects. In KDD conference, Newport Beach, California (pp. 215–218) New York: ACM.Google Scholar
  16. Kriegel, H.-P., Brecheisen, S., Kröger, P., Pfeifle, M., & Schubert, M. (2003). Using sets of feature vectors for similarity search on voxelized cad objects. In SIGMOD conference, San Diego, California (pp. 587–598).Google Scholar
  17. Kulldorff, M. (1997). A spatial scan statistic, Communications in Statistics: Theory and Methods, 26(6), 1481–1496.zbMATHMathSciNetGoogle Scholar
  18. Li, Y., Han, J., & Yang, J. (2004). Clustering moving objects. In KDD, Seattle, Washington (pp. 617–622).Google Scholar
  19. Nanni, M. (2002). Clustering methods for spatio-temporal data, PhD thesis, CS Department, University of Pisa, Italy.Google Scholar
  20. Saltenis, S., Jensen, C. S., Leutenegger, S. T., & Lopez, M. A. (2000). Indexing the positions of continuously moving objects. In Proceedings of ACM SIGMOD, Dallas, Texas (pp. 331–342). New York: ACM.Google Scholar
  21. Vlachos, M., Gunopulos, D., & Kollios, G. (2002). Discovering similar multidimensional trajectories. In ICDE, San Jose, California (pp. 673–684).Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.ISTI-Institute of CNRPisaItaly
  2. 2.Dipartimento di InformaticaUniversità di PisaPisaItaly

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