Key points are not available for this paper at this time.
This paper introduces a classification algorithm called phHyperCuts. Like the previously best known algorithm, HiCuts, HyperCuts is based on a decision tree structure. Unlike HiCuts, however, in which each node in the decision tree represents a hyperplane, each node in the HyperCuts decision tree represents a k--dimensional hypercube. Using this extra degree of freedom and a new set of heuristics to find optimal hypercubes for a given amount of storage, HyperCuts can provide an order of magnitude improvement over existing classification algorithms. HyperCuts uses 2 to 10 times less memory than HiCuts optimized for memory, while the worst case search time of HyperCuts is 50--500% better than that of HiCuts optimized for speed. Compared with another recent scheme, EGT-PC, HyperCuts uses 1.8--7 times less memory space while the worst case search time is up to 5 times smaller. More importantly, unlike EGT-PC, HyperCuts can be fully pipelined to provide one classification result every packet arrival time, and also allows fast updates.
Singh et al. (Mon,) studied this question.