Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization

  title={Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization},
  author={Napat Rujeerapaiboon and K. Schindler and D. Kuhn and W. Wiesemann},
  journal={SIAM J. Optim.},
  • Napat Rujeerapaiboon, K. Schindler, +1 author W. Wiesemann
  • Published 2019
  • Mathematics, Computer Science
  • SIAM J. Optim.
  • Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outlier cluster and performs cardinality-constrained K-means clustering on the residual dataset, treating the cluster cardinalities as a given input. We cast this problem as a mixed… CONTINUE READING
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