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.
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Kumpulainen, P., Hätönen, K. (2012). Characterizing Mobile Network Daily Traffic Patterns by 1-Dimensional SOM and Clustering. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_33
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DOI: https://doi.org/10.1007/978-3-642-32909-8_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
Online ISBN: 978-3-642-32909-8
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