Advertisement

Fixed-Resolution Growing Neural Gas for Clustering the Mobile Networks Data

  • Szabolcs Nováczki
  • Borislava GajicEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)

Abstract

An important property of the competitive neural models for data clustering is autonomous discovery of the data structure without a need of a priori knowledge. Growing Neural Gas (GNG) is one of the commonly used incremental clustering models that aims at preserving the topology and the distribution of the input data. Keeping the data distribution unchanged has already been recognized as a problem leading to bias sampling of input data. This is undesired for the use cases such as mobile network management and troubleshooting where is important to capture all the relevant network states uniformly. In this paper we propose a novel incremental clustering approach called Fixed Resolution GNG (FRGNG) that keeps the input data representation at the fixed resolution avoiding the oversampling and undersampling problems of original GNG algorithm. Furthermore, FRGNG introduces a native stopping criteria by terminating the run once the input data is represented with the desired fixed resolution. Additionally, the FRGNG has a potential of the algorithm acceleration which is especially important when large input data set is applied. We apply the FRGNG model to analyze the mobile network performance data and evaluate its benefits compared to GNG approach.

Keywords

Growing Neural Gas Fixed resolution mobile network clustering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 69, 43–59 (1982)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Fritzke, B.: A growing neural gas network learns topologies. In: Advances in Neural Information Processing Systems, vol. 6, no. 3, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
  3. 3.
  4. 4.
    Satizábal, H.F., Pérez-Uribe, A., Tomassini, M.: Prototype proliferation in the growing neural gas algorithm. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part II. LNCS, vol. 5164, pp. 793–802. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  5. 5.
    Cselenyi, Z.: Mapping the dimensionality, density and topology of data: The growing adaptive neural gas. Computer Methods and Programs in Biomedicine, 141–156 (2005)Google Scholar
  6. 6.
    Villmann, T., Claussen, J.C.: Magnification Control in Self-Organizing Maps and Neural Gas. Neural Computation, 446–449 (2006)Google Scholar
  7. 7.
    Marsland, S., Shapiro, J., Nehmzow, U.: A self-organizing network that grows when required. Neural Networks, 1041–1058 (2002)Google Scholar
  8. 8.
    Quintana-Pacheco, Y., Ruiz-Fernández, D., Magrans-Rico, A.: Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes. Neural Networks (2014). http://dx.doi.org/10.1016/j.neunet.2014.01.005
  9. 9.
    Canales, F., Chacón, M.: Modification of the growing neural gas algorithm for cluster analysis. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 684–693. Springer, Heidelberg (2007) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Nokia NetworksBudapestHungary
  2. 2.Nokia NetworksMunichGermany

Personalised recommendations