Biology Reference
In-Depth Information
[36]
A. K. Jain and R. C. Dubes. Algorithms for Clustering Data . Prentice-Hall Advanced
Reference Series. Prentice-Hall, Inc., 1988.
[37]
A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Computing
Surveys , 31(3):264-323, 1999.
[38]
R. E. Johnson. The role of cluster analysis in assessing comparability under the us
transfer pricing regulations. Business Economics , April 2001.
[39]
Y. Jung, H. Park, D. Du, and B. L. Drake. A decision criterion for the optimal number
of clusters in hierarchical clustering. Journal of Global Optimization , 25:91-111,
2003.
[40]
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing.
Science , 220(4598):671-680, 1983.
[41]
Y. Kluger, R. Barsi, J. T. Cheng, and M. Gerstein. Spectral biclustering of microarray
data: Coclustering genes and conditions. Genome Res. , 13(4):703-716, 2003.
[42]
T. Kohonen. Self Organization and Associative Memory . Springer Information Sci-
ence Series. Springer Verlag, 1989.
[43]
T. Kohonen. Self-Organizing Maps . Springer Verlag, Berlin, 1997.
[44]
L. Lazzeroni and A. Owen. Plaid models for gene expression data. Technical report,
Stanford University, 2000.
[45]
F. Leisch, A. Weingessel, and E. Dimitriadou. Competitive learning for binary valued
data. In L. Niklasson, M. Bod'en, and T. Ziemke, editors, Proceedings of the 8th
International Conference on Artificial Neural Networks (ICANN 98) , volume 2, pages
779-784, Skovde, Sweden, 1998. Springer.
[46]
A. Likas, N. Vlassis, and J. L. Vebeek. The global k-means clustering algorithm.
Pattern Recognition , 36:451-461, 2003.
[47]
X. Lin, C. Floudas, Y. Wang, and J. R. Broach. Theoretical and computational stud-
ies of the glucose signaling pathways in yeast using global gene expression data.
Biotechnology and Bioengineering , 84(7):864-886, 2003.
[48]
A. V. Lukashin and R. Fuchs. Analysis of temporal gene expression profiles: Clus-
tering by simulated annealing and determining the optimal number of clusters. Bioin-
formatics , 17(5):405-414, 2001.
[49]
J. McQueen. Some methods for classification and analysis of multivariate observa-
tions. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and
Probability , pages 281-297, 1967.
[50]
N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. J. Teller. Equation of
state calculations by fast computing machines. J. Chem. Phys. , 21:1087-1092, 1953.
[51]
G. E. Paules and C. A. Floudas. Apros-algorithmic developmeng for discrete-
continuous optimization problems. Operations Research , 37(6):902-915, 1989.
[52]
E. J. Pauwels and G. Fregerix. Finding salient regions in images: Non-parametric
clustering for image segmentation and grouping. Computer Vision and Image Under-
standing , 75:73-85, 1999.
[53]
P. Pipenbacher, A. Schliep, S. Schneckener, A. Schonhuth, D. Schomburg, and
R. Schrader. Proclust: Improved clustering of protein sequences with an extended
graph-based approach. Bioinformatics , 18(Supplement 2):S182-191, 2002.
[54]
W. M. Rand. Objective criteria for the evaluation of clustering methods. Journal of
American Statistical Association , pages 846-850, 1971.
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