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There is significant interest in the data mining and network management communities about the need to improve existing techniques for clustering multi-variate network traffic flow records so that we can quickly infer underlying traffic patterns. In this paper we investigate the use of clustering techniques to identify interesting traffic patterns from network traffic data in an efficient manner. We develop a framework to deal with mixed type attributes including numerical, categorical and hierarchical attributes for a one-pass hierarchical clustering algorithm. We demonstrate the improved accuracy and efficiency of our approach in comparison to previous work on clustering network traffic.
Traffic analysis, Network management, Network monitoring, Clustering, classification, and association rules
Parampalli Udaya, Abdun Naser Mahmood, Christopher Leckie, "An Efficient Clustering Scheme to Exploit Hierarchical Data in Network Traffic Analysis", IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 752-767, June 2008, doi:10.1109/TKDE.2007.190725
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