Robert Beverly and Karen Sollins.
Proceedings of USENIX Tackling Computer Systems Problems with Machine Lea rning Techniques (SysML 2008),
San Diego, CA, December 2008.

We pose partitioning a $b$-bit Internet Protocol (IP) address space as a supervised learning task. Given (\emph{IP, property}) labeled training data, we develop an IP-specific clustering algorithm that provides accurate predictions for unknown addresses in $O(b)$ run time. Our method offers a natural means to penalize model complexity, limit memory consumption, and is amenable to a non-stationary environment. Against a live Internet latency data set, the algorithm outperforms IP-na\"ive learning methods and is fast in practice. Finally, we show the model's ability to detect structural and temporal changes, a crucial step in learning amid Internet dynamics.

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