A Network Traffic Prediction Algorithm Based On Data Mining Technique
This paper is a description approach to predict
incoming and outgoing data rate in network system by using
association rule discover, which is one of the data mining
techniques. Information of incoming and outgoing data in each
times and network bandwidth are network performance
parameters, which needed to solve in the traffic problem. Since
congestion and data loss are important network problems. The result
of this technique can predicted future network traffic. In addition,
this research is useful for network routing selection and network
performance improvement.
<p>[1] Y. Wen, and T. Lee, “Fuzzy data mining and grey recurrent neural
network forecasting for traffic information systems,” in Proc. IEEE
International Conference on Information Reuse and Integration, pp.
356-361.
[2] Y. Hand, and F. Moutarde, “Analysis of Network-level Traffic States
using Locality Preservative Non-negative Matrix Factorization,” in
Proc. 14th IEEE Intelligent Transport Systems Conference
(ITSC'2011), Washington : United States,2011.
[3] T. Hauser, and W. Scherer, “Data mining tools for real time traffic
signal decision support and maintenance,” in Proc. IEEE International
Conference on Systems, 2001, 3: 1471-1477.
[4] B. Park, D. Lee, and H. Yun, “Enhancement of time of day based traffic
signal control,” in Proc. IEEE International Conference on Systems,
2003,4: 3619-3624.
[5] Xu, P., and S. Lin, “Internet traffic classification using C4.5 decision
tree,”. J. Softw., vol.20(10), pp. 2692-2704, 2009.
[6] L. Jia, L. Yang, Q. Kong, and S. Lin, “ Study of artificial immune
clustering algorithm and its applications to urban traffic control,” Int. J.
Inform. Technol., 2006, vol.12, pp.1-9.
[7] B. Raahemi, A. Kouznetsov, A. Hayajneh, and P. Rabinovitch,
“Classification of peer-to-peer traffic using incremental neural networks
(fuzzy ARTMAP),” in Proc. IEEE Canadian Conference on Electrical
and Computer Engineering, 2008, pp. 719-724.
[8] Z. Li, X. Yan, C. Yuan, J. Zhao ,and Z. Peng, “The fault diagnosis
approach for gears using multidimensional features and intelligent
classifier,” Imeche. Sem. Worldwide, vol.41, pp. 76-86, 2010.
[9] Z. Li, X. Yan, C. Yuan, J. Zhao, and Z. Peng, “Fault detection and
diagnosis of the gearbox in marine propulsion system based on
bispectrum analysis and artificial neural networks,” J. Mar. Sci. Appl.,
,vol.10, pp. 17-24, 2011.
[10] Z. Li, X. Yan, C. Yuan, Z. Peng, and L. Li, “Virtual prototype and
experimental research on gear multi-fault diagnosis using waveletautoregressive
model and principal component analysis method,” Mech.
Syst. Signal Pr ., vol. 25, pp.2589-2607, 2011.
[11] Z. Li, X. Yan, Y. Jiang, L. Qin ,and J. Wu, “A new data mining
approach for gear crack level identification based on manifold
learning,” Mechanika,vol 18, pp.29-34, 2012.
[12] Li, Z., X. Yan, Z. Guo, P. Liu, C. Yuan ,and Z. Peng, “A new intelligent
fusion method of multi-dimensional sensors and its application to tribosystem
fault diagnosis of marine diesel engines,” Tribol. Lett., vol.47,
pp. 1-15,2012.
[13] Li, Z., X. Yan, C. Yuan, and Z. Peng, “Intelligent fault diagnosis
method for marine diesel engines using instantaneous angular speed,” J.
Mech. Sci. Technol., vol. 26(8), pp. 2413-2423, 2012.
[14] M.J.A Berry, and G. S. Linnoff, “Data Mining Techniques for
Marketing, Sale and Customer Relationship Management,” New
York: Wiley Publishing, 2004.
[15] D. Ng’ambi “Pre_empting User Questions through Anticipation-
Data Mining FAQ Lists,” in Proc. of SAICSIT,2002,pp.101-109.
[16] N. Feamste, and J. Rexford, “Network-Wide BGP Route
Prediction for Traffic Engineering”. a Laboratory for Computer
Science, Massachusetts Institute of Technology, Cambridge, MA,
USA Internet and Networking Systems, AT&T Labs. Research,
Florham Park, NJ, USA, 2004.
[17] J. Han, and M. Kamber, “Data Mining Concepts and
Techniques,” USA : Morgan Kaufman,2001.</p>
<p>[1] Y. Wen, and T. Lee, “Fuzzy data mining and grey recurrent neural
network forecasting for traffic information systems,” in Proc. IEEE
International Conference on Information Reuse and Integration, pp.
356-361.
[2] Y. Hand, and F. Moutarde, “Analysis of Network-level Traffic States
using Locality Preservative Non-negative Matrix Factorization,” in
Proc. 14th IEEE Intelligent Transport Systems Conference
(ITSC'2011), Washington : United States,2011.
[3] T. Hauser, and W. Scherer, “Data mining tools for real time traffic
signal decision support and maintenance,” in Proc. IEEE International
Conference on Systems, 2001, 3: 1471-1477.
[4] B. Park, D. Lee, and H. Yun, “Enhancement of time of day based traffic
signal control,” in Proc. IEEE International Conference on Systems,
2003,4: 3619-3624.
[5] Xu, P., and S. Lin, “Internet traffic classification using C4.5 decision
tree,”. J. Softw., vol.20(10), pp. 2692-2704, 2009.
[6] L. Jia, L. Yang, Q. Kong, and S. Lin, “ Study of artificial immune
clustering algorithm and its applications to urban traffic control,” Int. J.
Inform. Technol., 2006, vol.12, pp.1-9.
[7] B. Raahemi, A. Kouznetsov, A. Hayajneh, and P. Rabinovitch,
“Classification of peer-to-peer traffic using incremental neural networks
(fuzzy ARTMAP),” in Proc. IEEE Canadian Conference on Electrical
and Computer Engineering, 2008, pp. 719-724.
[8] Z. Li, X. Yan, C. Yuan, J. Zhao ,and Z. Peng, “The fault diagnosis
approach for gears using multidimensional features and intelligent
classifier,” Imeche. Sem. Worldwide, vol.41, pp. 76-86, 2010.
[9] Z. Li, X. Yan, C. Yuan, J. Zhao, and Z. Peng, “Fault detection and
diagnosis of the gearbox in marine propulsion system based on
bispectrum analysis and artificial neural networks,” J. Mar. Sci. Appl.,
,vol.10, pp. 17-24, 2011.
[10] Z. Li, X. Yan, C. Yuan, Z. Peng, and L. Li, “Virtual prototype and
experimental research on gear multi-fault diagnosis using waveletautoregressive
model and principal component analysis method,” Mech.
Syst. Signal Pr ., vol. 25, pp.2589-2607, 2011.
[11] Z. Li, X. Yan, Y. Jiang, L. Qin ,and J. Wu, “A new data mining
approach for gear crack level identification based on manifold
learning,” Mechanika,vol 18, pp.29-34, 2012.
[12] Li, Z., X. Yan, Z. Guo, P. Liu, C. Yuan ,and Z. Peng, “A new intelligent
fusion method of multi-dimensional sensors and its application to tribosystem
fault diagnosis of marine diesel engines,” Tribol. Lett., vol.47,
pp. 1-15,2012.
[13] Li, Z., X. Yan, C. Yuan, and Z. Peng, “Intelligent fault diagnosis
method for marine diesel engines using instantaneous angular speed,” J.
Mech. Sci. Technol., vol. 26(8), pp. 2413-2423, 2012.
[14] M.J.A Berry, and G. S. Linnoff, “Data Mining Techniques for
Marketing, Sale and Customer Relationship Management,” New
York: Wiley Publishing, 2004.
[15] D. Ng’ambi “Pre_empting User Questions through Anticipation-
Data Mining FAQ Lists,” in Proc. of SAICSIT,2002,pp.101-109.
[16] N. Feamste, and J. Rexford, “Network-Wide BGP Route
Prediction for Traffic Engineering”. a Laboratory for Computer
Science, Massachusetts Institute of Technology, Cambridge, MA,
USA Internet and Networking Systems, AT&T Labs. Research,
Florham Park, NJ, USA, 2004.
[17] J. Han, and M. Kamber, “Data Mining Concepts and
Techniques,” USA : Morgan Kaufman,2001.</p>
@article{"International Journal of Information, Control and Computer Sciences:51828", author = "D. Prangchumpol", title = "A Network Traffic Prediction Algorithm Based On Data Mining Technique", abstract = "This paper is a description approach to predict
incoming and outgoing data rate in network system by using
association rule discover, which is one of the data mining
techniques. Information of incoming and outgoing data in each
times and network bandwidth are network performance
parameters, which needed to solve in the traffic problem. Since
congestion and data loss are important network problems. The result
of this technique can predicted future network traffic. In addition,
this research is useful for network routing selection and network
performance improvement.
", keywords = "Traffic prediction, association rule, data mining.", volume = "7", number = "7", pages = "890-4", }
