[1] | Chris, D. and Tao, Li. (2007). Adaptive Dimension Reduction using Discriminant Analysis and k-means Clustering, 24th International conference on machine learning, Corvallis USA, 521-528. |
[2] | Chris, D. and Xiaofeng, He. (2004). K-means Clustering via Principal Component Analysis, 21st International conference on machine learning, Canada, 29-36. |
[3] | Everitt, S. B., Stahl, D., Leese, M. and Landau, S. (2011). Cluster Analysis, 5th Ed., Willey series in Probability and Statistics, UK. |
[4] | Gowrilaksshmi, K. (2011). Clustering on High Dimensional Data that Reduces Dimensionality using Dimension Reduction Techniques, International Journal of Computer Applications in Engineering Sciences (IJCAES), 1, March 2011, 80-84. |
[5] | Halkidi, M., Batistakis, Y. and Vazirgiannis, M. (2001). On Clustering Validation Techniques, Journal of Intelligent Information Systems, 17:2/3, Kluwer Academic publishers, manufactured in the Netherlands, 107-145. |
[6] | Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, D., Antonellis, J., Scherf, U., Speed, P. (2003). Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data, Biostatistics, 4(2): 64- 249. |
[7] | Izenman, A. J. (2008). Modern Multivariate Statistical Techniques, Regression, Classification and Manifold learning, Springer Science & Business, Media Philadelphia, PA 19122, USA. |
[8] | Jonathan, M. G., Daniele, S. and Khhairul, A. R. (2010). Consensus Clustering and Fuzzy Classification for Breast Cancer Prognosis, 24th European Conference on Modeling and Simulation, June 1- 4th, Kuala Lumpur, Malaysia, 15-22. |
[9] | Joseph, F. H., Rolph, A. E., Ronald, L. T. and William, C.B. (2003) Multivariate Data Analysis, Fifth edition, published by Pearson Education, First Indian Reprint 2003, 493-496. |
[10] | Kumar, H. and Sharma, V. (2011). A Comparative Study of k-mean and PAM Algorithms using Leukemia Datasets, International symposium on computing, communication, and control, ISCCC, Proc. of CSIT ,Vol. 1(2011), IACSIT Press, Singapore , 136-140. |
[11] | Murillo J. and Rodriguez A. (2012). Linear dimensionality reduction with Gaussian mixture models, ICASSP, 27 March 2012, Japan. |
[12] | R manual documentation (2012). |
[13] | Robert M., Richard G., James H., (2003). Statistical design and analysis of Experiement with applications, Hoboken, N.J, Willey-2003, Pp. 98-104. |
[14] | Sanche, R., and Lonergan, K. (2006). Variable Reduction for Predictive Modeling with Clustering, Casually actuarial society forum, 89-100. |
[15] | San T. Roweis and Lowerence Saul (2000). Nonlinear dimensionality reduction by locally linear embedding, science Vil. 200, 22-Dec. 2000, pp 2323-2326. |
[16] | Sauerbrei, W. and Royston, P. (1999). Building Multivariable Prognostic and Diagnostic Models: transformation of the predictors by using fractional polynomials, Journal of the Royal Statistics Society Series A, 162(1), 71–94. |
[17] | Schumacher, M., Basert, G., Bojar, H., Huebner, K., Olschewski, M., Sauerbrei, W., Schmoor, C., Beyerle, C., Neumann, A. and Rauschecker, H. (1994). For the German Breast Cancer Study Group, Randomized 22 times trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients, Journal of Clinical Oncology, 12, 2086–2093. |
[18] | Shuiwang Ji, Jieping Ye., (2009). linear dimensionality reduction for multi-label classification, IJCA International joint conference on artificial intelligence, 17 Jul. 2009, 1077-1082. |