Splitting Methods for Convex Clustering

@article{Chi2015SplittingMF,
  title={Splitting Methods for Convex Clustering},
  author={Eric C. Chi and K. Lange},
  journal={Journal of Computational and Graphical Statistics},
  year={2015},
  volume={24},
  pages={1013 - 994}
}
  • Eric C. Chi, K. Lange
  • Published 2015
  • Mathematics, Medicine
  • Journal of Computational and Graphical Statistics
Clustering is a fundamental problem in many scientific applications. Standard methods such as k-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal. Recently introduced convex relaxations of k-means and hierarchical clustering shrink cluster centroids toward one another and ensure a unique global minimizer. In this work, we present two splitting methods for solving the convex clustering problem. The first is… Expand

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