Evaluating a branch-and-bound RLT-based algorithm for minimum sum-of-squares clustering

@article{Aloise2008EvaluatingAB,
  title={Evaluating a branch-and-bound RLT-based algorithm for minimum sum-of-squares clustering},
  author={Daniel Aloise and Pierre Hansen},
  journal={Journal of Global Optimization},
  year={2008},
  volume={49},
  pages={449-465}
}
Minimum sum-of-squares clustering consists in partitioning a given set of n points into c clusters in order to minimize the sum of squared distances from the points to the centroid of their cluster. Recently, Sherali and Desai (JOGO, 2005) proposed a reformulation-linearization based branch-and-bound algorithm for this problem, claiming to solve instances with up to 1,000 points. In this paper, their algorithm is investigated in further detail, reproducing some of their computational… CONTINUE READING

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