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  • C. Elkan
  • Computer Science
  • 2001 (First Publication: 4 August 2001)
  • This paper revisits the problem of optimal learning and decision-making when different misclassification errors incur different penalties. We characterize precisely but intuitively when a cost matrixExpand
  • C. Elkan
  • Computer Science
  • ICML
  • 2003 (First Publication: 21 August 2003)
  • The k-means algorithm is by far the most widely used method for discovering clusters in data. We show how to accelerate it dramatically, while still always computing exactly the same result as theExpand
  • Greg Hamerly, Charles Elkan
  • Computer Science
  • NIPS
  • 2003 (First Publication: 9 December 2003)
  • When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present an improved algorithm forExpand