Characterizing Mobile Network Daily Traffic Patterns by 1-Dimensional SOM and Clustering

  • Pekka Kumpulainen
  • Kimmo Hätönen
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)


Mobile network traffic produces daily patterns. In this paper we show how exploratory data analysis can be used to inspect the origin of the daily patterns. We use a 1-dimensional self-organizing map to characterize the patterns. 1-dimensional map enables compact visualization that is especially suitable for data where the variables are not independent but form a pattern. We introduce a stability index for analyzing the variation of the daily patterns of network elements along the days of the week. We use clustering to construct profiles for the network elements to study the stability of the traffic patterns within each element. We found out that the day of the week is the main explanation for the traffic patterns on weekends. On weekdays the traffic patterns are mostly specific to groups of networks elements, not the day of the week.


1-D SOM clustering mobile network daily pattern exploratory data analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Khedher, H., Valois, F., Tabbane, S.: Traffic characterization for mobile networks. In: 56th IEEE Vehicular Technology Conference, vol. 3, pp. 1485–1489. IEEE (2002)Google Scholar
  2. 2.
    Kumpulainen, P., Hätönen, K.: Compression of Cyclic Time Series Data. In: 12th IMEKO TC1 & TC7 Joint Symposium on Man Science & Measurement, pp. 413–419 (2008)Google Scholar
  3. 3.
    Kohonen, T.: Self-Organizing Map, 2nd edn. Springer, Berlin (1995)CrossRefGoogle Scholar
  4. 4.
    Kiviluoto, K.: Topology Preservation in Self-Organizing Maps. In: International Conference on Neural Networks (ICNN), pp. 294–299 (1996)Google Scholar
  5. 5.
    Vesanto, J.: SOM-based data visualization methods. Intelligent Data Analysis 3, 111–126 (1999)zbMATHCrossRefGoogle Scholar
  6. 6.
    Ultsch, A., Siemon, H.P.: Kohonen’s Self-Organizing Feature Maps for Exploratory Data Analysis. In: International Neural Network Conference, Dordrecht, Netherlands, pp. 305–308 (1990)Google Scholar
  7. 7.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in Matlab: the SOM toolbox. In: Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, pp. 35–40 (1999)Google Scholar
  8. 8.
    Kumpulainen, P., Hätönen, K., Knuuti, O., Alapaholuoma, T.: Internet traffic clustering using packet header information. In: 14th Joint International IMEKO TC1+TC7+TC13 Symposium, Jena, Germany (2011)Google Scholar
  9. 9.
    Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)CrossRefGoogle Scholar
  10. 10.
    Laiho, J., Raivio, K., Lehtimaki, P., Hätönen, K., Simula, O.: Advanced analysis methods for 3G cellular networks. IEEE Transactions on Wireless Communications 4(3), 930–942 (2005)CrossRefGoogle Scholar
  11. 11.
    Everitt, B., Landau, S., Leese, M.: Cluster analysis, 4th edn., Arnold (2001)Google Scholar
  12. 12.
    Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pekka Kumpulainen
    • 1
  • Kimmo Hätönen
    • 2
  1. 1.Department of Automation Science and EngineeringTampere University of TechnologyTampereFinland
  2. 2.Nokia Siemens NetworksT&S ResearchEspooFinland

Personalised recommendations