A Density-Based Clustering of Spatio-Temporal Data

  • Ehab ZaghloolEmail author
  • Saleh ElKaffas
  • Amani Saad
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 354)


Moving objects are one of many topics that have large data sets generated rapidly and continuously by spatial technologies. This paper focuses on the data mining of an example of such large data sets, spatio-temporal data. This research aims to predict future motion of moving objects regarding their location and time of arrival. A spatio-temporal algorithm is developed and presented which clusters sub-trajectories into similar groups taking into consideration the time dimension; time-aware, using a density based clustering technique. The presented algorithm partitions trajectories into smaller sub-trajectories then groups these segments based on a density-based clustering technique. Three different experiments are carried out, each one with a different data set. The results of each experiment are analyzed and predictions are made for the motion of each data set.


Data Mining Spatio-Temporal Data Density Based Clustering 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Parent, C., Pelekis, N., Theodoridis, Y., Yan, Z., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., Bogorny, V., Damiani, M., Gkoulalas-Divanis, A., Macedo, J.: Semantic trajectories modeling and analysis. CSUR 45, 1–32 (2013)CrossRefGoogle Scholar
  2. 2.
    Pelekis, N., Kopanakis, I., Kotsifakos, E., Frentzos, E., Theodoridis, Y.: Clustering uncertain trajectories. Knowl. Inf. Syst. 28, 117–147 (2010)CrossRefGoogle Scholar
  3. 3.
    Antonio, J., Rocha, R., Oliveira, G., Alvares, L., Bogorny, V.: DB-SMoT: A Direction-Based Spatio-Temporal Clustering Method. In: 5th IEEE Int. Conference on Intelligent Systems (IS), pp. 114–119 (2010)Google Scholar
  4. 4.
    Pelekis, N., Theodoulidis, B., Kopanakis, I., Theodoridis, Y.: Literature review of spatio-temporal database models. Knowl. Eng. Rev. 19 (2004)Google Scholar
  5. 5.
    Kalyani, D., Chaturvedi, S.: A Survey on Spatio-Temporal Data Mining. Int. Journal of Computer Science and Network (IJCSN) 1 (2012)Google Scholar
  6. 6.
    Kisilevich, S., Mansmann, F., Nanni, M., Rinzivillo, S.: Spatio-temporal clustering. In: Data Mining and Knowledge Discovery Handbook, pp. 855–874 (2010)Google Scholar
  7. 7.
    Lee, J., Han, J., Whang, K.: Trajectory clustering: a partitionand-group framework. In: SIGMOD Conference, pp. 593–604 (2007)Google Scholar
  8. 8.
    Pelekis, N., Andrienko, G., Andrienko, N., Kopanakis, I., Marketos, G., Theodoridis, Y.: Visually exploring movement data via similarity-based analysis. Journal of Intelligent Information Systems 38, 343–391 (2011)CrossRefGoogle Scholar
  9. 9.
    Corporation, U.: Unisys Weather - Atlantic Tropical Storm Tracking by Year,
  10. 10. Data Files (ASCII Text Format),

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.College of Engineering and Technology, Arab Academy for ScienceTechnology and Maritime TransportAlexandriaEgypt
  2. 2.College of Computing and Information Technology, Arab Academy for ScienceTechnology and Maritime TransportAlexandriaEgypt

Personalised recommendations