참고문헌
- Cheong, D. and Oh, K. J. (2014). Using cluster analysis and genetic algorithm to develop portfolio investment strategy based on investor information. Journal of the Korean Data & Information Science Society, 25, 107-117. https://doi.org/10.7465/jkdi.2014.25.1.107
- Choi, S. S., Cha, S. H. and Tappert, C. (2010). A survey of binary similarity and distance measures. Journal on Systemics, Cybernetics and Informatics, 8, 43-48.
- Jang, H., Kim, K. K. and Kang, C. (2014). Comparison of clustering methods for categorical data. Journal of the Korean Data Analysis Society, 16, 2439-2445.
- Jeong, K. M. (2005). A note on Bayesian information criterion in model-based clustering. Journal of the Korean Data Analysis Society, 7, 1517-1529.
- Kim, D. (2009). On the Silhouette plot in cluster analysis. Journal of the Korean Data Analysis Society, 11, 2955-2964.
- Kim, M., Jeon, J., Woo, K. and Kim, M. (2010). A new similarity measure for categorical attribute-based clustering. Journal of Korean Institute of Information Scientists and Engineers : Databases, 37, 71-81.
- Lee, K. A. and Kim, J. H. (2011). Comparison of clustering with yeast microarray gene expression data. Journal of the Korean Data & Information Science Society. 22, 741-753.
- Lim, J. S. and Lim, D. H. (2012). Comparison of clustering methods of microarray gene expression data. Journal of the Korean Data & Information Science Society, 23, 39-51. https://doi.org/10.7465/jkdi.2012.23.1.039
- Meyer A. (2002) Comparison of similarity coefficients used in cluster analysis with dominant markers data, MSc Thesis, Universidade de Sao Paulo, Piracicaba.
- Oh, S. M., Song, J. M. and Kim, C. S. (2012). Clustering analysis using the influence of attributes in categorical data analysis. Journal of the Korean Institute of Information Scientists and Engineers, 18, 790-793.
- Park, H. C. (2009). An introduction to statistical database, Changwon National University Press, Changwon.
- Park, H. C. (2011). Association rule thresholds of similarity measures considering negative co-occurrence frequencies. Journal of the Korean Data & Information Science Society, 22, 1113-1122.
- Park, H. C. (2012). Exploration of PIM based similarity measures as association rule thresholds. Journal of the Korean Data & Information Science Society, 23, 1127-1135. https://doi.org/10.7465/jkdi.2012.23.6.1127
- Park, H. C. (2013). Proposition of causal association rule thresholds. Journal of the Korean Data & Information Science Society, 24, 1189-1197. https://doi.org/10.7465/jkdi.2013.24.6.1189
- Park, H. J. and Kim, J. T. (2013). Classification of universities in Daegu.Gyungpook by support vector cluster analysis. Journal of the Korean Data & Information Science Society. 24, 783-791. https://doi.org/10.7465/jkdi.2013.24.4.783
- Ryu, J. Y. and Park, H. C. (2013). A study on Jaccard dissimilarity measures for negative association rule generation. Journal of the Korean Data Analysis Society, 15, 3111-3121.
- Warrens, M. J. (2008). Bounds of resemblance measures for binary (presence/absence) variables. Journal of Classification, 25, 195-208. https://doi.org/10.1007/s00357-008-9024-6
- Woo, S. Y., Lee, J. W. and Jhun, M. (2014). Microarray data analysis using relative hierarchical clustering. Journal of the Korean Data & Information Science Society, 25, 999-1009. https://doi.org/10.7465/jkdi.2014.25.5.999
- Yeo, I. K. (2011). Clustering analysis of Korea's meteorological data. Journal of the Korean Data & Information Science Society. 22, 941-949.