A distance-based point-reassignment heuristic for the k-hyperplane clustering problem. Amaldi, E. & Coniglio, S. *European Journal of Operational Research*, 227(1):22-29, Elsevier B.V., 5, 2013.

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Paper Website abstract bibtex

We consider the k-Hyperplane Clustering problem where, given a set of m points in Rn, we have to partition the set into k subsets (clusters) and determine a hyperplane for each of them, so as to minimize the sum of the squares of the Euclidean distances between the points and the hyperplane of the corresponding clusters. We give a nonconvex mixed-integer quadratically constrained quadratic programming formulation for the problem. Since even very small-size instances are challenging for state-of-the-art spatial branch-and-bound solvers like Couenne, we propose a heuristic in which many "critical" points are reassigned at each iteration. Such points, which are likely to be ill-assigned in the current solution, are identified using a distance-based criterion and their number is progressively decreased to zero. Our algorithm outperforms the best available one proposed by Bradley and Mangasarian on a set of real-world and structured randomly generated instances. For the largest instances, we obtain an average improvement in the solution quality of 54%. © 2012 Elsevier B.V. All rights reserved.

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