Mining Frequent Patterns from Network Data Flow

Authors:
Xin Li;Zhi-Hong Deng;Hao Ma;Shi-Wei Tang;Bei Zhang
Affiliations:
Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871 and Dalian Commodity Exchange, Dalian, 1160 ...;Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871;Computer Center, Peking University, Beijing 100871;Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871;Computer Center, Peking University, Beijing 100871
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

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Abstract

The main objective of network monitoring is to discover the event patterns that happen frequently. In this paper, we have intensively studied the techniques used to mine frequent patterns from network data flow. We devel-oped a powerful class of algorithms to deal with a series of problems when min-ing frequent patterns from network data flow. We experimentally evaluate our algorithms on real datasets collected from the campus network of Peking Uni-versity. The experimental results show these algorithms are efficient.