Stability-based validation of clustering solutions

Neural Computation

Volumn 16, Issue 6, 2004, Pages 1299-1323

  Lange, Tilman ^a   Roth, Volker ^a   Braun, Mikio L ^b   Buhmann, Joachim M ^a  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPARATIVE STUDY; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; HUMAN; LEUKEMIA; LYMPHOMA; METHODOLOGY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; LEUKEMIA; LYMPHOMA; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; SENSITIVITY AND SPECIFICITY;

EID: 2442611856     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976604773717621     Document Type: Article

References (32)

1. Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Saber, H., Tran, T., Yu, A., Powell, J. I., Yang, L., Marti, G. E., Moore, T., Judson, J. Jr., Lu, L., Lewis, D. B., Tibshirani, R., Sherlock, G., Chan, W. C., Greiner, T. C., Weisenburger, D. D., Armitage, J. O., Warnke, R., Levy, R., Wilson, W., Grever, M. R., Byrd, J. C., Botstein, D., Brown, P. O., & Staudt, L. M. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403, 503-511.
2. Ben-Hur, A., Elisseeff, A., & Guyon, I. (2002). A stability based method for discovering structure in clustered data. In Pacific Symposium on Biocomputing 2002 (pp. 6-17). Singapore: World Scientific.
3. Bittner, M., Meltzer, P., Chen, Y., Jiang, Y., Seftor, E., Hendrix, M., Radmacher, S., R., Yakhini, Z., Ben-Dor, A., Sampas, N., Dougherty, E., Wang, E., Marincola, F., Gooden, C., Lueders, J., Glatfelter, A., Pollock, P., Carpten, J., Gillanders, E., Leja, D., Dietrich, K., Beaudry, C., Berens, M., Alberts, D., & Sondak, V. (2000). Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature, 406(3), 536-540.
4. Breckenridge, J. (1989). Replicating cluster analysis: Method, consistency and validity. Multivariate Behavioral Research, 24, 147-161.
5. Buhmann, J. M. (1995). Data clustering and learning. In M. Arbib (Ed.), Handbook of brain theory and neural networks (pp. 278-282). Cambridge, MA: MIT Press.
6. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification. New York: Wiley.
7. Dudoit, S., & Fridlyand, J. (2002). A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology, 3(7). Available on-line: http://genomebiology.com/2002/317/research/0036.
8. Eisen, M., Spellman, P., Botstein, D., & Brown, P. (1998). Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA, 95, 14863-14868.
9. Fischer, B., Zöller, T., & Buhmann, J. M. (2001). Path based pairwise data clustering with application to texture segmentation. In M. A. T. Figueiredo, J. Zerubia, & A. K. Jain (Eds.), LNCS energy minimization methods in computer vision and pattern recognition. Berlin: Springer-Verlag.
10. Fowlkes, E., & Mallows, C. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78, 553-584.
11. Fraley, C., & Raftery, A. (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis. Computer Journal, 41(8), 578-588.
12. Golub, T. T., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiui, M. A., Bloomfield, C. D., & Lander, E. S. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
13. Gordon, A. D. (1999). Classification (2nd ed.). London: Chapman & Hall.
14. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements ofstatistical learning: Data mining, inference and prediction. New York: Springer-Verlag.
15. Hofmann, T., & Buhmann, J. M. (1997). Pairwise data clustering by deterministic annealing. IEEE PAMI, 19(1), 1-14.
16. Iyer, V. R., Eisen, M. B., Ross, D. T., Schuler, G., Moore, T., Lee, J. C. F., Trent, J. M., Staudt, L. M., Hudson, J. Jr., Boguski, M. S., Lashkari, D., Shalon, D., Botstein, D., & Brown, P. O. (1999). The transcriptional program in the response of human fibroblasts to serum. Science, 283(1), 83-87.
17. Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Upper Saddle River, NJ: Prentice Hall.
18. Jain, A. K., Murty, M., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 265-323.
19. Kuhn, H. (1955). The Hungarian method for the assignment problem. Naval Res. Logist. Quart., 2, 83-97.
20. Lander, E. (1999). Array of hope. Nature Genetics Supplement, 21, 3-4.
21. Lange, T., Braun, M., Roth, V., & Buhmann, J. (2003). Stability-based model selection. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems, 15 (pp. 617-624). Cambridge, MA: MIT Press.
22. Levine, E. (1999). Un-supervised estimation of cluster validity-methods and applications. Master's thesis, Weizmann Institute of Science.
23. Levine, E., & Domany, E. (2001). Resampling method for unsupervised estimation of cluster validity. Neural Computation, 13, 2573-2593.
24. Rose, K., Gurewitz, E., & Fox, G. (1992). Vector quantization and deterministic annealing. IEEE Trans. Inform. Theory, 38(4), 1249-1257.
25. Roth, V., & Lange, T. (in press). Bayesian class discovery in microarray datasets. IEEE Transactions on Biomedical Engineering.
26. Shamir, R., & Sharan, R. (2001). Algorithmic approaches to clustering gene expression data. In T. Jiang, T. Smith, Y. Xu, & M. Q. Zhang (Eds.), Current topics in computational biology. Cambridge, MA: MIT Press.
27. Sharan, R., & Shamir, R. (2000). CLICK: A clustering algorithm with applications to gene expression analysis. In ISMB'00 (pp. 307-316). Menlo Park, CA: AAAI Press.
28. Smyth, P. (1998). Model selection for probabilistic clustering using cross-validated likelihood (Tech. Rep. 98-09). Irvine, CA: Information and Computer Science, University of California, Irvine.
29. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., & Golub, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. PNAS, 96, 2907-2912.
30. Tibshirani, R., Walther, G., Botstein, D., & Brown, P. (2001). Cluster validation by prediction strength (Tech. Rep.). Stanford, CA: Statistics Department, Stanford University.
31. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters via the gap statistic. J. Royal Statist. Soc. B, 63(2), 411-423.
32. Yeung, K., Haynor, D., & Ruzzo, W. (2001). Validating clustering for gene expression data. Bioinformatics, 17(4), 309-316.