ONE METHOD FOR INTERNET TRAFFIC TRENDS DETECTION
Article devoted to the problem of trends detection in Internet traffic. For solution of this problem we has proposed to use the algorithm of sequential patterns mining. Detecting trends in Internet traffic is very necessary to make the right decisions in the management of computer networks and to allow choosing the basic requirements and possible metrics to monitor them. (pp. 38-46)
- Zeng B. D., Zhang W. Li, Zhang M., Hong Q. An adaptive sampling methodology for internet traffic data measurement / Proceedings of the International Conference on Communication Software and Networks, 2009, Feb. 27–28, pp.215–218.
- Agrawal R., Srikant R. Mining Sequential Patterns // Journal Intelligent Systems, 1997, vol.9, no.1, pp.33–56.
- Agrawal R., Srikant R. Mining sequential patterns: Generalizations and performance improvements / Proceedings of the 5th International Conference on Extending Database Technology, 1996, pp.1–17.
- Han J., Kamber M., Data mining: concepts and techniques. Morgan Kaufmann, 2006.
- Lamparter O. and Stauffer B., A network traffic measurement tool / Proceedings of the 10th International Conference on Telecommunications, 2003, Feb. 23-Mar., vol.1, pp.1078–1083.
- Zhanh L., Tang J. Characterization and performance study of IP traffic in WDM networks // Computer communications, 2001, no.24, pp.1702–1713.
- Paxson V. Empirically derived analytic models of wide-area TCP connections / IEEE / ACM Trans. Netw., 1994, vol.2, no.4, pp.316–336.
- Paxson V. and Floyd S., Wide area trafﬁc: the failure of Poisson modeling / IEEE/ACM Trans. Netw., 1995, vol.3, no.3, pp.226–244.
- Karagiannis T., Papagiannaki K., Faloutsos M. BLINC: multilevel trafﬁc classiﬁcation in the dark / Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Pomputer Communications, 2005, New York, pp.229–240.
- Zaki M. J. Spade: An eﬃcient algorithm for mining frequent sequences // Machine Learning 2001, vol.42, no.1–2, pp.31–60.
- Pei J., Han J., Mortazavi-Asl B., etc. Mining sequential patterns by pattern-growth: The preﬁxspan approach / IEEE Transactions on Knowledge and Data Engineering 2004, vol.16, no.11, pp.1424–1440.
- Zaki M.J. Scalable data mining for rules, Technical Report Ph.D. Dissertation, University of Rochester, New York, 1998.
- Ming-Yen Lin, Suh-Yin Interactive Sequence Discovery by Incremental Mining // An International Journal of Information Sciences-Informatics and Computer Science, 2004, vol.165, no.3–4, pp.187–205.
- Mabroukeh RN., Ezeife C. I. A taxonomy of sequential pattern mining algorithms // Journal ACM Computing Surveys, 2010, vol.43, no. 3.
- Chandra V. Shekhar Rao, Sammula P. Survey on Sequential Pattern Mining Algorithms / International Journal of Computer Applications, 2013, vol.76, no.12, pp.24–31.
- Parikh M., Chaudhari B. and Chand C., A Comparative Study of Sequential Pattern Mining Algorithms // International Journal of Application or Innovation in Engineering and Management, 2013, vol.2, no.2, pp.103–109.
- Agrawal R. and Srikant R., Mining sequential patterns. Research Report RJ9910, IBM Almaden Research Center, San Jose, California, October 1994.
- Pei J., Han J., Mortazavi-Asl B., etc. PrefixSpan: mining sequential patterns efficiently by prefix projected pattern growth / Proceedings of the 17th International Conference on Data Engineering, 2001, pp.215–226.