Free Access
Issue
ESAIM: PS
Volume 16, 2012
Page(s) 436 - 452
DOI https://doi.org/10.1051/ps/2012013
Published online 04 September 2012
  1. D. Arthur and S. Vassilvitskii, қ-means++ : the advantages of careful seeding, in Proc. of SODA (2007). [Google Scholar]
  2. S. Ben-David and U. von Luxburg, Relating clustering stability to properties of cluster boundaries, in Proc. of COLT (2008). [Google Scholar]
  3. S. Ben-David, U. von Luxburg and D. Pál, A sober look on clustering stability, in Proc. of COLT (2006). [Google Scholar]
  4. S. Ben-David, D. Pál and H.-U. Simon, Stability of қ-means clustering, in Proc. of COLT (2007). [Google Scholar]
  5. L. Bottou and Y. Bengio, Convergence properties of the қ-means algorithm, in Proc. of NIPS (1995). [Google Scholar]
  6. S. Dasgupta and L. Schulman, A probabilistic analysis of EM for mixtures of separated, spherical Gaussians. J. Mach. Learn. Res. 8 (2007) 203–226. [Google Scholar]
  7. S. Graf and H. Luschgy, Foundations of Quantization for Probability Distributions. Springer (2000). [Google Scholar]
  8. D. Hochbaum and D. Shmoys, A best possible heuristic for the -center problem. Math. Operat. Res. 10 (1985) 180–184. [CrossRef] [Google Scholar]
  9. T. Lange, V. Roth, M. Braun and J. Buhmann, Stability-based validation of clustering solutions. Neural Comput. 16 (2004) 1299–1323. [CrossRef] [PubMed] [Google Scholar]
  10. R. Ostrovsky, Y. Rabani, L.J. Schulman and C. Swamy, The effectiveness of Lloyd-type methods for the қ-means problem, in Proc. of FOCS (2006). [Google Scholar]
  11. O. Shamir and N. Tishby, Cluster stability for finite samples, in Proc. of NIPS (2008). [Google Scholar]
  12. O. Shamir and N. Tishby, Model selection and stability in қ-means clustering, in Proc. of COLT (2008). [Google Scholar]
  13. O. Shamir and N. Tishby, On the reliability of clustering stability in the large sample regime, in Proc. of NIPS (2008). [Google Scholar]
  14. N. Srebro, G. Shakhnarovich and S. Roweis, An investigation of computational and informational limits in Gaussian mixture clustering, in Proc. of ICML (2006). [Google Scholar]
  15. Z. Zhang, B. Dai and A. Tung, Estimating local optimums in EM algorithm over Gaussian mixture model, in Proc. of ICML (2008). [Google Scholar]

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