Abstract
Mobile network is a continuously evolving system. Recognizing the behaviour of the subscribers helps the operator in managing and developing the services they offer. Valuable information for those purposes can be revealed by analyzing the daily traffic patterns which result from the usage habits of the subscribers. In this paper we show how exploratory data analysis can be used to discover valuable information from the daily patterns. We present generic tools and methods that are especially suitable for data that consists of patterns. The proposed procedure consists of four parts: preprocessing, day of the week analysis, network element analysis and anomaly detection. The methods are based on clustering and one dimensional self organizing map. One dimensional map enables compact visualization that is especially suitable for data where the variables are not independent but form a pattern. 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. The results on the mobile network traffic are presented on a general level. Detailed analysis of the root causes behind the findings requires knowledge about the configuration, topology and geography of the network, which are available only for the operator.
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References
Bezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans Syst Man Cybern B 28:301–315
Chilton MA, Bloodgood JM (2007) The Dimensions of tacit & explicit knowledge: a description and measure. In: Proceedings of the 40th Hawaii international conference on system sciences, p 188a
de Oliveira JV, Pedrycz W (2007) Advances in fuzzy clustering and its applications. Wiley, Chichester
Duda RO, Hart PE, Stork DG (2001) Pattern Classification, 2nd edn. Wiley, New York
Everitt B, Landau S, Leese M (2001) Cluster analysis. Arnold, London
Gnanadesikan R, Kettenring JR, Tsao SL (1995) Weighting and selection of variables for cluster analysis. J Classif 2(1):113–136
Hätönen K (2009). Data mining for telecommunications network log analysis. PhD Thesis, University of Helsinki
Holma H, Hooli K, Kinnunen P, Kolding T, Marsch P, Wang X (2012) Coordinated Multipoint Transmission and Reception. In: Holma H, Toskala A, (eds) LTE-Advanced, 3GPP solution for IMT-advanced. Chap 13. Wiley, New York, pp 184–205
Khedher H, Valois F, Tabbane S (2002) Traffic characterization for mobile networks. In: 56th IEEE vehicular technology conference, vol 3, pp 1485–1489
Kiviluoto K (1996) Topology Preservation in Self-Organizing Maps. In: International conference on neural networks (ICNN), pp 294–299
Kohonen T (1995) Self-organizing map. Springer, Berlin
Kruskal WH (1960) Some remarks on wild observations. Technometrics 2(1):1–3
Kumpulainen P, Hätönen K (2008a) Local anomaly detection for mobile network monitoring. Inf Sci 178(20):3840–3859
Kumpulainen P, Hätönen K (2008b) Compression of Cyclic Time Series Data. In: 12th IMEKO TC1 & TC7 Joint Symposium on Man Science & Measurement, pp 413–419
Kumpulainen P, Hätönen K (2012) Characterizing Mobile Network Daily Traffic Patterns by 1-Dimensional SOM and Clustering. In: Proceedings of 13th EANN conference CCIS311, pp 325–333
Kumpulainen P, Hätönen K, Knuuti O, Alapaholuoma T (2011) Internet traffic clustering using packet header information. In: 14th Joint International IMEKO TC1 + TC7 + TC13 Symposium
Laiho J, Raivio K, Lehtimaki P, Hätönen K, Simula O (2005) Advanced analysis methods for 3G cellular networks. IEEE Trans Wireless Commun 4(3):930–942
Liquid Net, NSN (2013) http://www.nokiasiemensnetworks.com/portfolio/liquidnet. referenced 25.3.2013
Maronna RA, Martin RD, Yohai VJ (2006) Robust statistics: theory and methods. Wiley, Chichester
Meinicke P, Lingner T, Kaever A, Feussner K, Göbel C, Feussner I, Karlovsky P, Morgenste B (2008) Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps. Algorithms Mol Biol 3:9. doi:10.1186/1748-7188-3-9
Milligan GW, Cooper MC (1988) A Study of standardization of variables in cluster analysis. J Classif 5:181–204
Portnoy L, Eskin E, Stolfo S (2001) Intrusion detection with unlabeled data using clustering. In: Proceedings of ACM CSS workshop on data mining applied to security, p 14
Ultsch A, Siemon HP (1990) Kohonen’s self-organizing feature maps for exploratory data analysis. In: international neural network conference, pp 305–308
van der Heijden F, Duin R, de Ridder D, Tax DMJ (2004) classification, parameter estimation and state estimation: an engineering approach using MATLAB. Wiley, New York
Vesanto J (1999) SOM-based data visualization methods. Intell Data Anal 3:111–126
Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Networks 11(3):586–600
Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (1999) Self-organizing map in Matlab: the SOM toolbox. In Proceedings of the Matlab DSP conference, pp 35–40
Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244
Xu R, Wunsch DC II (2009). Clustering. IEEE Press, New York
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Kumpulainen, P., Hätönen, K. EANN 2012: exploratory analysis of mobile phone traffic patterns using 1-dimensional SOM, clustering and anomaly detection. Evolving Systems 4, 251–265 (2013). https://doi.org/10.1007/s12530-013-9091-8
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DOI: https://doi.org/10.1007/s12530-013-9091-8