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4. Brandsma, T., Buishand, T.A.: Simulation of extreme precipitation in the Rhine basin by
nearest-neighbour resampling. Hydrology and Earth System Sciences 2, 195-209 (1998)
5. Breckenridge, J.: Replicating cluster analysis: Method, consistency and validity.
Multivariate Behavioral Research (1989)
6. Das, A.K., Sil, J.: Cluster Validation using Splitting and Merging Technique. In: Int. Conf.
on Computational Intelligence and Multimedia Applications, ICCIMA (2007)
7. Davison, A.C., Hinkley, D.V., Young, G.A.: Recent developments in bootstrap
methodology. Statistical Science 18, 141-157 (2003)
8. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2001)
9. Estivill-Castro, V., Yang, J.: Cluster Validity Using Support Vector Machines.
In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737,
pp. 244-256. Springer, Heidelberg (2003)
10. Faceli, K., Marcilio, C.P., Souto, D.: Multi-objective Clustering Ensemble. In: Proceedings
of the Sixth International Conference on Hybrid Intelligent Systems (2006)
11. Fern, X.Z., Lin, W.: Cluster Ensemble Selection. In: SIAM International Conference on
Data Mining (2008)
12. Fred, A., Jain, A.K.: Combining Multiple Clusterings Using Evidence Accumulation.
IEEE Trans. on Pattern Analysis and Machine Intelligence 27(6), 835-850 (2005)
13. Fred, A., Jain, A.K.: Data Clustering Using Evidence Accumulation. In: Intl. Conf. on
Pattern Recognition, ICPR 2002, Quebec City, pp. 276-280 (2002)
14. Fred, A., Jain, A.K.: Learning Pairwise Similarity for Data Clustering. In: Int. Conf. on
Pattern Recognition (2006)
15. Fred, A., Lourenco, A.: Cluster Ensemble Methods: from Single Clusterings to Combined
Solutions. SCI, vol. 126, pp. 3-30 (2008)
16. Fridlyand, J., Dudoit, S.: Applications of resampling methods to estimate the number of
clusters and to improve the accuracy of a clustering method. Stat. Berkeley Tech. Report
No. 600 (2001)
17. Inokuchi, R., Nakamura, T., Miyamoto, S.: Kernelized Cluster Validity Measures and
Application to Evaluation of Different Clustering Algorithms. In: IEEE Int. Conf. on
Fuzzy Systems, Canada, July 16-21 (2006)
18. Law, M.H.C., Topchy, A.P., Jain, A.K.: Multiobjective data clustering. In: IEEE
Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 424-430 (2004)
19. Lange, T., Roth, V., Braun, M.L., Buhmann, J.M.: Stability-based validation of clustering
solutions. Neural Computation 16(6), 1299-1323 (2004)
20. Minaei-Bidgoli, B., Topchy, A., Punch, W.F.: Ensembles of Partitions via Data
Resampling. In: Intl. Conf. on Information Technology, ITCC 2004, Las Vegas (2004)
21. Möller, U., Radke, D.: Performance of data resampling methods based on clustering.
Intelligent Data Analysis 10(2) (2006)
22. Rakhlin, A., Caponnetto, A.: Stability of k-means clustering. In: Advances in Neural
Information Processing Systems, vol. 19. MIT Press, Cambridge (2007)
23. Roth, V., Lange, T.: Feature Selection in Clustering Problems. In: Advances in Neural
Information Processing Systems (2004)
24. Roth, V., Lange, T., Braun, M., Buhmann, J.: A Resampling Approach to Cluster
Validation. In: Intl. Conf. on Computational Statistics, COMPSTAT (2002)
25. Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining
multiple partitions. Journal of Machine Learning Research 3, 583-617 (2002)
26. Xie, X.L., Beni, G.: A Validity measure for Fuzzy Clustering. IEEE Trans. on Pattern
Analysis and Machine Intelligence 13(4), 841-846 (1991)
 
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