References
-
김재희 (2008).
, 교우사, 서울 -
김재희 (2011).
, 교우사, 서울 - Cho, K. and Park, H. (2008). A study of association rule application using self-organizung map for fused data. Journal of the Korean Data & Information Science Society, 19, 95-104.
- Fraley, C. and Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97, 611-631. https://doi.org/10.1198/016214502760047131
- Fraley, C. and Raftery, A. E. (2006). MCLUST Version 3 for R: Normal mixture modeling and model-based clustering, Technical Report No. 504.
- Fraley, C. and Raftery, A. E. (2007). Bayesian regularization for normal mixture estimation and modelbased clustering. Journal of Classification, 24, 155-181. https://doi.org/10.1007/s00357-007-0004-5
- Gentleman, R., Caray, V. J., Huber, W., Irizarry, R. A. and Dudoit, S. (2005). Bioinformatics and computational biology solutions using R and bioconductor, Spinger, New York.
- Getz, G., Levine, E., Domany, E. and Zhang, M. Q. (2000). Super-paramagneic clustering of yeast expression profiles. Physica A, 279, 457-464. https://doi.org/10.1016/S0378-4371(99)00524-5
- Handl, J., Knowles, J. and Kell, D. B. (2005). Computational cluster validation in post-genomic data analysis. Bioinformatics, 21, 3201-3212. https://doi.org/10.1093/bioinformatics/bti517
- Kim, J. and Kim, H. (2008). Clustering of change patterns using Fourier coefficients. Bioinformatics, 24, 184-191. https://doi.org/10.1093/bioinformatics/btm568
- Kim, J. and Ko, Y. (2009). A comparison of cluster analyses and clutering of sensory data on Hanwoo bulls. The Korean Journal of Applied Statistics, 22, 745-758. https://doi.org/10.5351/KJAS.2009.22.4.745
- Kaufman, L. and Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis, Wiley, New York.
- Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21, 1-6. https://doi.org/10.1016/S0925-2312(98)00030-7
- Lee, Y. and An, M. (2007). A comparison of clustering algorithm in data mining. Journal of the Korean Data & Information Science Society, 14, 725-736.
- McLachlan, G. J., Do, K.-A. and Ambroise C. (2004). Analyzing microarray gene expression data, Wiley, New York.
- Park, C. (2007). Monitoring of gene regulations using average rank in DNA microarray: Implement of R. Journal of the Korean Data & Information Science Society, 18, 1005-1021.
- Park, H. and Ryu, J. (2005). Clustering algorithm using a center of gravity for grid-based sample. Journal of the Korean Data & Information Science Society, 16, 217-226.
- Rousseeuw, P. T. (1987). Silhouettes : A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
- Toronen, R., Kolehmainen, M., Wong, G. and Castren, E. (1999). Analysis of gene expression data using self-organizing maps. Federation of European Biochemical Societies, 451, 142-146. https://doi.org/10.1016/S0014-5793(99)00524-4
- Wit, E. and McClure, J. (2004). Statistics for microarrays, Wiley, New York.
- Yeung, K. Y., Haynor D. R. and Ruzzo, W. L. (2000). Validating clustering for gene expression data. Bioinformatics, 17, 309-318.
- Zhang, L., Zhang, A. and Ramanathan, M. (2003). Fourier harmonic approach for visualizing temporal patterns of gene expression data. IEEE Computer Society Bioinformatics Conference, 2, 137-147.