Free Access
Volume 17, 2013
Page(s) 370 - 418
Published online 21 May 2013
  1. D. Angluin and L. Valiant, Fast probabilistic algorithms for Hamiltonian circuits. J. Comput. Syst. Sci. 18 (1979) 155–193. [CrossRef] [Google Scholar]
  2. G. Biau, B. Cadre and B. Pelletier, A graph-based estimator of the number of clusters. ESAIM: PS 11 (2007) 272–280. [CrossRef] [EDP Sciences] [Google Scholar]
  3. M. Brito, E. Chavez, A. Quiroz and J. Yukich, Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection. Stat. Probab. Lett. 35 (1997) 33–42. [CrossRef] [MathSciNet] [Google Scholar]
  4. S. Bubeck and U. von Luxburg, Nearest neighbor clustering: a baseline method for consistent clustering with arbitrary objective functions. J. Mach. Learn. Res. 10 (2009) 657–698. [Google Scholar]
  5. J.W. Harris and H. Stocker, Handbook of Mathematics and Computational Science. Springer (1998). [Google Scholar]
  6. W. Hoeffding, Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58 (1963) 13–30. [CrossRef] [MathSciNet] [Google Scholar]
  7. D.O. Loftsgaarden and C.P. Quesenberry, A nonparametric estimate of a multivariate density function. Ann. Math. Stat. 36 (1965) 1049–1051. [CrossRef] [Google Scholar]
  8. M. Maier, M. Hein and U. von Luxburg, Optimal construction of k-nearest neighbor graphs for identifying noisy clusters. Theoret. Comput. Sci. 410 (2009) 1749–1764. [CrossRef] [MathSciNet] [Google Scholar]
  9. M. Maier, U. von Luxburg and M. Hein, Influence of graph construction on graph-based clustering measures, in Advances in Neural Information Processing Systems, vol. 21, edited by D. Koller, D. Schuurmans, Y. Bengio and L. Bottou. MIT Press (2009) 1025–1032. [Google Scholar]
  10. G. Miller, S. Teng, W. Thurston and S. Vavasis, Separators for sphere-packings and nearest neighbor graphs. J. ACM 44 (1997) 1–29. [CrossRef] [MathSciNet] [Google Scholar]
  11. H. Narayanan, M. Belkin and P. Niyogi, On the relation between low density separation, spectral clustering and graph cuts, in Advances in Neural Information Processing Systems, vol. 19, edited by B. Schölkopf, J. Platt and T. Hoffman. MIT Press (2007) 1025–1032. [Google Scholar]
  12. A. Srivastav and P. Stangier, Algorithmic Chernoff-Hoeffding inequalities in integer programming. Random Struct. Algorithms 8 (1996) 27–58. [CrossRef] [Google Scholar]
  13. U. von Luxburg, A tutorial on spectral clustering. Stat. Comput. 17 (2007) 395–416. [CrossRef] [Google Scholar]
  14. U. von Luxburg, M. Belkin and O. Bousquet, Consistency of spectral clustering. Ann. Stat. 36 (2008) 555–586. [CrossRef] [Google Scholar]

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