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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)

Abstract

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.

Keywords

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

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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

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