[1] M. J. Callow, S. Dudoit, E. L. Gong, T. P. Speed and E. M. Rubin, Microarray expression profiling identifies genes with altered expression in HDL deficient mice, Genome Res. 10 (2000), 2022-2029.
[2] D. Dembele and P. Kastner, Fuzzy c-means method for clustering microarray data, Bioinformatics 19 (2003), 973-980.
[3] I. Dhilon, E. Marcotte and U. Roshan, Diametrical clustering for identifying anticorrelated gene clusters, Bioinformatics 19 (2003), 1612-1619.
[4] S. Dudoit and J. Fridlyand, Bagging to improve the accuracy of a clustering procedure, Biometrics 19 (2003), 1090-1099.
[5] M. Dugas, S. Merk, S. Breit and P. Dirschedl, Mdclust: Exploratory microarray analysis by multidimensional clustering, Bioinformatics 20 (2004), 931-936.
[6] M. B. Eisen, P. Spellman, P. O. Brown and D. Botstein, Cluster analysis and display of genome-wide expression patterns, P. Natl. Acad. Sci. USA 95 (1998), 14863-14868.
[7] C. Fraley and A. E. Raftery, MCLUST: Software for model-based clustering, discriminant analysis and density estimation, Technical Report no. 415, Department of Statistics, University of Washington, 2002.
[8] J. A. Hartigan and M. A. Wong, A k-means clustering algorithm, Appl. Stat. 28 (1979), 126-130.
[9] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, New York, 2002.
[10] R. Herwig, A. J. Poustka, C. Meuller, H. Lehrach and J. O?Brien, Large-scale clustering of cDNAfingerprinting data, Genome Res. 9(11) (1999), 1093-1105.
[11] D. Horn and I. Axel, Novel clustering algorithm for microarray expression data in a truncated svd space, Bioinformatics 19 (2003), 1110-1115.
[12] T. R. Hughes, M. J. Marton, A. R. Jones, C. J. Roberts, R. Stoughton, C. D. Armour, H. A. Bennett, E. Coffey and Y. D. He, Functional discovery via a compendium of expression profiles, Cell 102 (2000), 109-126.
[13] L. Kaufman and P. J. Rousseeuw, Finding Groups in a Data, Wiley, New York, 1990.
[14] T. Kohonen, The self-organizing map, Proc. IEEE 78 (1990), 1464-1479.
[15] M. T. Lee, F. C. Kuo, G. A. Whitmore and J. Sklar, Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations, P. Natl. Acad. Sci. USA 97 (2000), 9834-9839.
[16] A. Lukashin and R. Fuchs, Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters, Bioinformatics 17 (2001), 405-414.
[17] F. Luo, L. Khan, F. Bastani, I. L. Yen and J. Zhou, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, 2004.
[18] G. J. McLachlan, R. W. Bean and D. Peel, A mixture model-based approach to the clustering of microarray expression data, Bioinformatics 18 (2002), 1-10.
[19] M. Medvedovic, K. Y. Yeung and R. E. Bumgarner, Bayesian mixture model based clustering of replicated microarray data, Bioinformatics 8 (2004), 1222-1232.
[20] J. Qin, D. Lewis and W. Noble, Kernel hierarchical gene clustering from microarray gene expression data, Bioinformatics 19 (2003), 2097-2104.
[21] D. Ridder, F. Staal, J. M. van Dogen and M. J. Reinders, Maximum significance clustering of oligonucleotide microarrays, Bioinformatics 22 (2006), 326-331.
[22] M. Schena, D. Shalon, R. W. Davis and P. O. Brown, Quantitative monitoring of gene expression patterns with complementary DNA microarray, Science 270 (1995), 467-470.
[23] R. Sharan, A. Maron-Katz and R. Shamir, Clik and expander: a system for clustering and visualizing gene expression data, Bioinformatics 19 (2003), 1787-1799.
[24] R. Sharan and R. Shamir, CLICK: a clustering algorithm with applications to gene expression analysis, Proc. ISMB (2000), 307-316.
[25] G. Sherlock, Analysis of large-scale gene expression data, Curr. Opin. Immunol. 12 (2000), 201-205.
[26] R. Steuer, J. Kurths, C. Daub, J. Weise and J. Selbig, The mutual information: detecting and evaluating dependencies between variables, Bioinformatics 18 (2002), 231-240.
[27] Z. Szallasi and R. Somogyi, Genetic network analysis-the millennium opening version, Proc. PSBC Tutorial, 2001.
[28] P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E. S. Lander and T. R. Golub, Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation, P. Natl. Acad. Sci. USA 96 (1999), 2907-2912.
[29] S. Tavazoide, J. Hughes, M. Campbell, R. J. Cho and G. M. Churo, Systematic determination of genetic network architecture, Nat. Genet. 22 (1999), 281-285.
[30] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, 1999.
[31] S. Varma and R. Simon, Iterative class discovery and feature selection using Minimal Spanning Trees, BMC Bioinformatics 5 (2004), 126-134.
[32] X. Wen, S. Fuhrman, G. S. Michaels, D. B. Carr, S. Smith, J. L. Barker and R. Somogyi, Large-scale temporal gene expression mapping of central nervous system development, P. Natl. Acad. Sci. USA 95 (1998), 334-339.
[33] Y. Xu, V. Olman and D. Xu, Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees, Bioinformatics 18 (2002), 536-545.
[34] Y. Xu, V. Olman, L. Wang and D. Xu, Clustering EXCAVATOR: a computer program for efficiently mining gene expression data, Nucleic Acids Res. 31 (2003), 5582-5589.
[35] K. Yeung, D. Haynor and W. Ruzzo, Validating clustering for gene expression data, Bioinformatics 17 (2001), 309-318.
[36] K. Y. Yeung, M. Medvedovic and R. E. Bumgarner, Clustering gene expression data with repeated measurements, Genome Biol. 4 (2003), R34.
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