참고문헌
- R. O. Duda, P. E. Hart and Da. G. Stork, "Pattern Classification (2nd Edition)", Wiley-Interscience, Oct., 2000.
- J. Vesanto, J. Himberg, E. Alhoniemi and J. Parkankangas, "Self-Organizing Map in Matlab: the SOM Toolbox", Proceedings of the Matlab DSP Conference, pp. 34-40, 1999.
- M. H. Yang and N. Ahuja, "A Data Partition Method for Parallel Self-Organizing Map", Proceeding of the IJCNN 99, pp. 1929-1933, 1999.
- Z. Huang, "Extension to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values", Data Mining and Knowledge Discovery, Vol 2, pp. 283-304, 1998. https://doi.org/10.1023/A:1009769707641
- D. Pelleg and A. Moore, "Accelerating Exact K-means Algorithms with Geometric Reasoning", International Conference on Knowledge Discovery and Datamining '99, pp. 277-281, 1999.
- A. K. Jain, "Data clustering: 50 years beyond K-means", Pattern Recognition Letters, Vol. 31, pp. 651-666, 2010. https://doi.org/10.1016/j.patrec.2009.09.011
- M. Figueiredo and A. K. Jain, "Unsupervised learning of finite mixture models", IEEE transactions on pattern analysis and machine intelligence, Vol. 24, pp. 381-396, 2002. https://doi.org/10.1109/34.990138
- R. Tibshirani, G. Walther and T. Hastie, "Estimating the number of clusters in a data set via the gap statistic", Journal of the royal statistical society, Vol. 63, pp. 411-423, 2001. https://doi.org/10.1111/1467-9868.00293
- C. Rasmussen, "The infinite gaussian mixture model", Advances in neural information processing systems, Vol. 12, pp. 554-560, 2000.
- D. Pelleg and A. Moore, "X-means: Extending k-means with efficient estimation of the number of clusters", In Proc. of the Seventeenth International Conference on Machine Learning (ICML2000), June, pp. 727-734, 2000.
- S. Salvador and P. Chan, "Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms", In Proc. of the 16th IEEE International Conference on Tools with Artificial Intelligence, Nov., pp. 576-584, 2004.
- W. Lu and I. Traore, "Determining the optimal number of clusters using a new evolutionary algorithm", In Proc. Of the 17th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 05), Nov., 2 pp., 2005.
- B. Boutsinas, D. K. Tasoulis and M. N. Vrahatis, "Estimating the number of clusters using a windowing technique", Journal of Pattern Recognition an Image Analysis, Vol. 16, No. 2, April, pp. 143-154, 2006. https://doi.org/10.1134/S1054661806020015
- 지태창, 이현진, 이일병, "온라인 문서 군집화에서 군집 수 결정 방법", 정보처리학회지, Vol. 117, pp. 513-522, 2007.
- O. Satoshi and T. Katsumi, "How Many Objects?: Determining the Number of Clusters with a Skewed Distribution", Proceeding of the 18th European Conference on Artificial Intelligence, pp. 771-772, 2008.
- R. V. Ranga, "Incremental Clustering Algorithm for Earth Science Data Mining", Proceeding of the 9th International Conference on Computational Science, pp. 375-384, 2009.
- A. J. Graaff and A. P. Engelbrecht, "Using sequential deviation to dynamically determine the number of clusters found by a local network neighbourhood artificial immune system", Journal of Applied Soft Computing archive, Vol. 11, pp. 2698-2713, 2011. https://doi.org/10.1016/j.asoc.2010.10.017
- Earl Gose, Richard Johnsonbugh and Steve Jost, "Pattern Recognition and Image Analysis", Prentice Hall, 1996.
- Y. Yang, "Can the strength of AIC and BIC be shared?", Biometrika, Vol. 92, pp. 937-950, 2005. https://doi.org/10.1093/biomet/92.4.937
- D. D. Lewis, "Reuters-21578 text categorization test collection distribution 1.0", http://www.research.att.com/-lewis, 1999.
- S. Hettich and S. D. Bay, "The UCI KDD Archive [http://kdd.ics.uci.edu]", Irvine, CA: University of California, Department of Information and Computer Science, 1999.
피인용 문헌
- 소셜 네트워크 분석을 위한 동적 하위 그룹 생성 vol.14, pp.1, 2011, https://doi.org/10.9728/dcs.2013.14.1.41
- 차세대 융합형 콘텐츠 산업 분류체계에 관한 연구 vol.14, pp.1, 2011, https://doi.org/10.9728/dcs.2013.14.1.97