Maximum volume clustering: a new discriminative clustering approach
The Journal of Machine Learning Research
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We study the relation between a class of 0-1 integer linear programs and their rational relaxations. We show that the rational optimum to a problem instance can be used to construct a provably good 0-1 solution by means of a randomized algorithm. Our technique can be extended to provide bounds on the disparity between the rational and 0-1 optima for a given problem instance.