LP cutting-plane methods for K-means extend to fair and spectral clustering, certifying 1% optimality on most n≤3000 instances and often beating popular heuristics.
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A scalable linear programming-based framework for data clustering
LP cutting-plane methods for K-means extend to fair and spectral clustering, certifying 1% optimality on most n≤3000 instances and often beating popular heuristics.