A Survey on Clustering Techniques for Situation Awareness

  • Stefan Mitsch
  • Andreas Müller
  • Werner Retschitzegger
  • Andrea Salfinger
  • Wieland Schwinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)


Situation awareness (SAW) systems aim at supporting assessment of critical situations as, e.g., needed in traffic control centers, in order to reduce the massive information overload. When assessing situations in such control centers, SAW systems have to cope with a large number of heterogeneous but interrelated real-world objects stemming from various sources, which evolve over time and space. These specific requirements harden the selection of adequate data mining techniques, such as clustering, complementing situation assessment through a data-driven approach by facilitating configuration of the critical situations to be monitored. Thus, this paper aims at presenting a survey on clustering approaches suitable for SAW systems. As a prerequisite for a systematic comparison, criteria are derived reflecting the specific requirements of SAW systems and clustering techniques. These criteria are employed in order to evaluate a carefully selected set of clustering approaches, summarizing the approaches’ strengths and shortcomings.


Data Mining Cluster Technique Situation Awareness Cluster Trajectory Incremental 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aggarwal, C.C., et al.: A framework for clustering evolving data streams. In: Proc. of 29th Int. Conf. on Very Large Data Bases, pp. 81–92. VLDB Endowment (2003)Google Scholar
  2. 2.
    Andrienko, G., Andrienko, N.: Interactive cluster analysis of diverse types of spatio-temporal data. ACM SIGKDD Explorations Newsletter 11(2), 19–28 (2009)CrossRefGoogle Scholar
  3. 3.
    Baumgartner, N., et al.: Beaware!—situation awareness, the ontology-driven way. Int. Journal of Data and Knowledge Engineering 69(11), 1181–1193 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Birant, D., Kut, A.: St-dbscan: An algorithm for clustering spatial and temporal data. Data & Knowledge Engineering 60(1), 208–221 (2007)CrossRefGoogle Scholar
  5. 5.
    Chen, J., Lai, C., Meng, X., Xu, J., Hu, H.: Clustering moving objects in spatial networks. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 611–623. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 226–231. AAAI Press (1996)Google Scholar
  7. 7.
    Cao, F., et al.: Density-based clustering over an evolving data stream with noise. In: SIAM Conf. on Data Mining, pp. 328–339 (2006)Google Scholar
  8. 8.
    Gaber, M.M., et al.: Mining data streams: a review. ACM SIGMOD Record 34(2), 18–26 (2005)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gaffney, S., Smyth, P.: Trajectory clustering with mixtures of regression models. In: Proc. of the 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD 1999, pp. 63–72. ACM (1999)Google Scholar
  10. 10.
    Gariel, M., et al.: Trajectory clustering and an application to airspace monitoring. Trans. Intell. Transport. Sys. 12(4), 1511–1524 (2011)CrossRefGoogle Scholar
  11. 11.
    Han, J., et al.: Spatial clustering methods in data mining: A survey. In: Geographic Data Mining and Knowledge Discovery, pp. 1–29 (2011)Google Scholar
  12. 12.
    Han, J., et al.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann (2011)Google Scholar
  13. 13.
    Ikonomovska, E., et al.: A survey of stream data mining. In: Proc. of 8th National Conf. with Int. Participation, ETAI (2007)Google Scholar
  14. 14.
    Iyengar, V.S.: On detecting space-time clusters. In: Proc. of 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 587–592. AAAI Press (1996)Google Scholar
  15. 15.
    Jain, A.K., et al.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)CrossRefGoogle Scholar
  16. 16.
    Jensen, C., et al.: Continuous clustering of moving objects. IEEE Transactions on Knowledge and Data Engineering 19(9), 1161–1174 (2007)CrossRefGoogle Scholar
  17. 17.
    Jeung, H., et al.: Discovery of convoys in trajectory databases. Proc. VLDB Endow. 1(1), 1068–1080 (2008)Google Scholar
  18. 18.
    Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 364–381. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Kalyani, D., Chaturvedi, S.K.: A survey on spatio-temporal data mining. Int. Journal of Computer Science and Network (IJCSN) 1(4) (2012)Google Scholar
  20. 20.
    Kavitha, V., Punithavalli, M.: Clustering time series data stream - a literature survey. Int. Journal of Computer Science and Inf. Sec. (IJCSIS) 8(1) (2010)Google Scholar
  21. 21.
    Kisilevich, S., et al.: Spatio-temporal clustering: a survey. In: Data Mining and Knowledge Discovery Handbook, pp. 1–22 (2010)Google Scholar
  22. 22.
    Kranen, P., et al.: The clustree: Indexing micro-clusters for anytime stream mining. Knowledge and Information Systems Journal 29(2), 249–272 (2011)CrossRefGoogle Scholar
  23. 23.
    Li, Y., et al.: Clustering moving objects. In: Proc. of the 10th ACM Int. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 617–622. ACM (2004)Google Scholar
  24. 24.
    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
  25. 25.
    Lu, C.-T., Lei, P.-R., Peng, W.-C., Su, I.-J.: A framework of mining semantic regions from trajectories. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011, Part I. LNCS, vol. 6587, pp. 193–207. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  26. 26.
    Matheus, C., et al.: Sawa: An assistant for higher-level fusion and situation awareness. In: Proc. of SPIE Conf. on Multisensor, Multisource Information Fusion. Architectures, Algorithms, and Applications, pp. 75–85 (2005)Google Scholar
  27. 27.
    Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. Journal of Intelligent Information Systems 27(3), 267–289 (2006)CrossRefGoogle Scholar
  28. 28.
    Neill, D.B., et al.: Detection of emerging space-time clusters. In: Proc. of the 11th ACM SIGKDD Int. Conf. on Knowledge Discovery in Data Mining, KDD 2005, pp. 218–227. ACM (2005)Google Scholar
  29. 29.
    Geetha, R., et al.: A survey of spatial, temporal and spatio-temporal data mining. Journal of Computer Applications 1(4), 31–33 (2008)MathSciNetGoogle Scholar
  30. 30.
    Rosswog, J., Ghose, K.: Detecting and tracking spatio-temporal clusters with adaptive history filtering. In: Proc. of 8th IEEE Int. Conf. on Data Mining, Workshops (ICDMW), pp. 448–457 (2008)Google Scholar
  31. 31.
    Rosswog, J., Ghose, K.: Detecting and tracking coordinated groups in dense, systematically moving, crowds. In: Proc. of the 12th SIAM Int. Conf. on Data Mining, pp. 1–11. SIAM/Omnipress (2012)Google Scholar
  32. 32.
    Tai, C.-H., Dai, B.-R., Chen, M.-S.: Incremental clustering in geography and optimization spaces. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 272–283. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  33. 33.
    Wang, M., Wang, A., Li, A.: Mining spatial-temporal clusters from geo-databases. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 263–270. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  34. 34.
    Warren Liao, T.: Clustering of time series data-a survey. Pattern Recogn. 38(11), 1857–1874 (2005)zbMATHCrossRefGoogle Scholar
  35. 35.
    Wimmer, M., et al.: A survey on uml-based aspect-oriented design modeling. ACM Computing Surveys 43(4), 28:1–28:33 (2011)Google Scholar
  36. 36.
    Xu, R., Wunsch II, D.C.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)CrossRefGoogle Scholar
  37. 37.
    Zheng, K., et al.: On discovery of gathering patterns from trajectories. In: IEEE Int. Conf. on Data Engineering, ICDE (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefan Mitsch
    • 2
  • Andreas Müller
    • 1
  • Werner Retschitzegger
    • 1
  • Andrea Salfinger
    • 1
  • Wieland Schwinger
    • 1
  1. 1.Johannes Kepler University LinzLinzAustria
  2. 2.Computer Science Dept.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations