References
- 김성수 (1999). 통계그래픽스를 이용한 K-평균 및 계층적 군집분석, <한국분류학회지>, 3, 13-27
- 허명회, 이용구 (2004). K-평균 군집화의 재현성 평가 및 응용, <응용통계연구>, 17, 135-144 https://doi.org/10.5351/KJAS.2004.17.1.135
- Banfield, J. D. and Raftery, A. E. (1993). Model-based Gaussian and non-Gaussian clustering, Biometrics, 49, 803-821 https://doi.org/10.2307/2532201
- Brusco, M. J. and Cradit, J. D. (2001). A variable-selection heuristic for K-means clustering, Psychometrika, 66, 249-270 https://doi.org/10.1007/BF02294838
- Chen, J. S., Ching, R. K. H. and Lin, Y. S. (2004). An extended study of the K-means algorithm for data clustering and its applications, The Journal of the Operational Research Society, 55, 976-987 https://doi.org/10.1057/palgrave.jors.2601732
- Dasgupta, A. and Raftery, A. E. (1998). Detecting features in spatial point processes with clutter via modelbased clustering, Journal of the American Statistical Association, 93, 294-302 https://doi.org/10.2307/2669625
- Everitt, B. S., Landau, S. and Leese, M. (2001). Cluster Analysis, Arnold, London
- Fraley, C. (1998). Algorithms for model-based gaussian hierarchical clustering, SIAM Journal on Scientific Computing, 20, 270-281 https://doi.org/10.1137/S1064827596311451
- Fraley, C. and Raftery, A. E. (1998). How many clusters? Which clustering methods? Answers via modelbased cluster analysis, The Computer Journal, 41, 578-588 https://doi.org/10.1093/comjnl/41.8.578
- Fraley, C. and Raftery, A. E. (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report No. 504, Department of Statistics University of Washington
- Hartigan, J. A. and Wong, M. A. (1979). A K-means clustering algorithm, Applied Statistics, 28, 100-108 https://doi.org/10.2307/2346830
- Kim, S. S., Kwon, S. and Cook, D. (2000). Interactive visualization of hierarchical clusters using MDS and MST, Metrika, 51, 39-51 https://doi.org/10.1007/s001840000043
- Krzanowski, W. J. (1988). Principles of Multivariate Analysis, Oxford Science, Oxford
- Milligan, G. and Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set, Psychometrika, 50, 159-179 https://doi.org/10.1007/BF02294245
- Mojena, R. (1977). Hierarchical grouping methods and stopping rules: An evaluation, The Computer Journal, 20, 359-363 https://doi.org/10.1093/comjnl/20.4.359
- Mojena, R., Wishart, D. and Andrews, G. B. (1980). Stopping rules for Wards'clustering method, COMPSTAT, 426-432
- Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods, Journal of American Statistical Association, 66, 846-850 https://doi.org/10.2307/2284239
- SPSS (2000). Clementine Application Templates for Telecommunication Industries(Telco CAT), Chicago, SPSS Inc.
- Stanford, D. C. and Raftery, A. E. (2000). Principal curve clustering with noise, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 601-609 https://doi.org/10.1109/34.862198
- Ward, J. H. (1963). Hierarchical grouping to optimize an objective function, Journal of American Statistical Association, 58, 236-244 https://doi.org/10.2307/2282967
- Wehrens, R., Buydens, L. M. C., Fraley, C. and Raftery, A. E. (2004). Model-based clustering for image segmentation and large data sets via sampling, Journal of Classification, 21, 231-253 https://doi.org/10.1007/s00357-004-0018-8
Cited by
- A Variable Selection Procedure for K-Means Clustering vol.25, pp.3, 2012, https://doi.org/10.5351/KJAS.2012.25.3.471
- Variable Selection and Outlier Detection for Automated K-means Clustering vol.22, pp.1, 2015, https://doi.org/10.5351/CSAM.2015.22.1.055