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Characterizing ADSL customer behaviours by network traffic data-mining

Analyse des usages adsl à partir de données de trafic

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Abstract

The very rapid adoption of new applications by some segments of the adsl customers may have a strong impact on the quality of service delivered to all customers. This makes the segmentation of adsl customers according to their network usage a critical step both for a better understanding of the market and for the prediction and dimensioning of the network. Relying on a “bandwidth only” perspective to characterize network customer behaviour does not allow the discovery of usage patterns in terms of applications. In this paper, we shall describe how data mining techniques applied to network measurement data can help to extract qualitative and quantitative knowledge.

Résumé

L’adoption, en général très rapide, de nouvelles applications par certaines catégories des clients adsl peut impacter fortement la qualité de service offerte à l’ensemble des clients et rend critique la bonne connaissance de la manière dont ces clients utilisent le réseau haut débit au sens des applications, tant d’un point de vue marketing que réseau, pour l’adaptation et le dimensionnement des infrastructures. Les analyses orientées „réseau” ne permettent pas de découvrir précisément les usages des clients au sens des applications. Dans cet article, nous décrivons différentes techniques d’exploration de données qui permettent de construire une connaissance qualitative et quantitative des usages à partir de données de trafic observées.

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References

  1. Lelong (B.), Beaudouin (V.), Usages domestiques d’Internet, Nouveaux terminaux et hauts débits. 3e Colloque International sur les Usages et Services des Télécommunications, ENST, Paris, France, 2001.

    Google Scholar 

  2. Anderson (B.), Gale (C.), Jones (M. L. R.), McWilliams (A.), Domesticating broadband-What consumers really do with flat-rate, always-on and fast Internet connections, BT Technology Journal, 20, 1, pp. 103–114, 2002.

    Google Scholar 

  3. Clement (H.), Lautard (D.), Ribeyron (M.), ADSL traffic: a forecasting model and the present reality in France, WTC’2002, Paris, France, 2002.

    Google Scholar 

  4. Kohonen (T.), Self-Organizing Maps. Springer-Verlag, Heidelberg, 2001.

    Book  MATH  Google Scholar 

  5. Oja (E.), Kaski (S.), Kohonen maps. Elsevier, 1999.

    Google Scholar 

  6. Vesanto (J.), Alhoniemi (E.), Clustering of the self organizing map, IEEE Transactions of Neural Networks, 11, 3, pp. 586–600, 2000.

    Google Scholar 

  7. Lemaire (V.), Clerot (F.), SOM-based clustering for on line fraud behaviour classification: a case study, FSKD, 2002.

    Google Scholar 

  8. Vesanto (J.), Himberg (J.), Alhoniemi (E.), Parhankangas (J.), SOM Toolbox for Matlab 5. Technical Report A57, Helsinki University of Technology, Neural Networks Research Centre, 2000.

    Google Scholar 

  9. Clerot (C.), Fessant (F.), From IP port numbers to ADSL customer segmentation: knowledge aggregation and representation using Kohonen maps, DATAMINING IV, Rio de Janeiro, Brazil, 2003.

    Google Scholar 

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Correspondence to Françoise Fessant.

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Fessant, F., François, J. & Clérot, F. Characterizing ADSL customer behaviours by network traffic data-mining. Ann. Telecommun. 62, 350–368 (2007). https://doi.org/10.1007/BF03253265

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  • DOI: https://doi.org/10.1007/BF03253265

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