A Novel Network Traffic Analysis Method Based on Fuzzy Association Rules
- 658 Downloads
For network traffic analysis and forecasting, a novel method based on fuzzy association rules is proposed in this paper. Connecting fuzzy logic theory with association rules, the method sets up the fuzzy association rules and could analyze the traffic of the global network by using data mining algorithm. Therefore, this method can represent the traffic’s characters much more precisely and forecast the behaviors of traffic in advance. The paper firstly introduces the new classification method on network traffic. Then the fuzzy association rules are applied to analyze the behaviors of traffic in existence. Finally, the results of simulation experiments indicating that the fuzzy association rule is very effective in discovering the relativity of different traffic in the analysis of traffic flow are shown.
KeywordsMembership Function Association Rule Traffic Flow Network Traffic Membership Degree
Unable to display preview. Download preview PDF.
- 1.Awduche, D.O., Chin, A., Flwalid, A., et al.: A framework for internet traffic engineering. internet-draft, draft-ietf-tewg-framework-00.txt , http://www.ietf.org
- 2.Lee, Y.D., van de Liefvoort, A., Wallace, V.L.: Modeling correlated traffic with a generalized IPP. Performance Evaluation, 99–114 (2000)Google Scholar
- 4.Xinyu, Y., Shouqi, Z., Ming, Z., Li, Z., Hengyi, W.: The Path Restrained Association Rules Algorithmic for Network Traffic Engineering. Xi’an Jiao Tong University transaction 8, 834–838 (2001)Google Scholar
- 5.Delgado, M., Marin, N., Sanchez, D., Vila, M.-A.: Fuzzy association rules: general model and applications. Fuzzy Systems 11(2) (2003)Google Scholar
- 6.Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th VLDB Conferences, Santiago Chile (1994)Google Scholar
- 7.Tzungpei, H., Chansheng, K., Shengchai, C.: Mining Fuzzy Sequential Patterns from Quantitative Data. Systems, Man, and Cybernetics 3, 12–15 (1999)Google Scholar
- 8.Han, J., Kamber, M.: Data mining concepts and techniques. 1st edn. China Machine Press, Beijing (2001)Google Scholar
- 9.Naiqian, L., Junyi, S.: An Algorithm Automatic Generating Fuzzy Sets for Quantitative Attributes. Computer Engineering and Application 21, 10–11 (2002)Google Scholar
- 10.Shu, J.Y., Tsang, E.C.C., Yeung, D.S.: Query fuzzy association rules in relational database. In: IFSA World Congress and 20th NAFIPS International Conference (2001)Google Scholar