참고문헌
- J. Nayak, B. Naik, and H. S. Behera, "Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014," in Computational Intelligence in Data Mining-Volume 2, New Delhi: Springer India, pp. 133-149, 2015.
- D. B. Hassen, H. Taleb, I. B. Yaacoub, and N. Mnif, "Classification of chest lesions with using fuzzy c-means algorithm and support vector machines," in International Joint Conference SOCO'13-CISIS'13-ICEUTE'13, Cham: Springer International Publishing, pp. 319-328, 2014.
- N. Bharill and A. Tiwari, "Handling big data with fuzzy based classification approach," in Advance Trends in Soft Computing, Cham: Springer International Publishing, pp. 219-227, 2014.
- S. R. Kannan, S. Ramathilagam, A. Sathya, and R. Pandiyarajan, "Effective fuzzy c-means based kernel function in segmenting medical images," Computers in Biology and Medicine, vol. 40, no. 6, pp. 572-579, 2010. https://doi.org/10.1016/j.compbiomed.2010.04.001
- X. Wang, Y. Wang, and L. Wang, "Improving fuzzy c-means clustering based on feature-weight learning," Pattern Recognition Letters, vol. 25, no. 10, pp. 1123-1132, 2004. https://doi.org/10.1016/j.patrec.2004.03.008
- R. M. Esteves and C. Rong, "Using Mahout for clustering Wikipedia's latest articles: a comparison between k-means and fuzzy c-means in the cloud," in Proceedings of IEEE 3rd International Conference on Cloud Computing Technology and Science (CloudCom), Athens, Greece, 2011, pp. 565-569.
- Q. Yu and Y. Dai, "Parallel fuzzy C-means algorithm based on MapReduce," Computer Engineering and Applications, vol. 49, no. 14, pp. 133-137, 2013.
- J. Q. Zhang, X. W. Zheng, and H. P. Wu, "Research on fuzzy C-means clustering algorithm parallel," Microcomputer & Its Applications, vol. 29, no. 23, pp. 8-18, 2010.
- D. Irfan, X. Xu, S. Deng, and Z. He, "S-Canopy: a featurebased clustering algorithm for supplier categorization," in Proceedings of IEEE 4th Conference on Industrial Electronics and Applications, Xi'an, China, 2009, pp. 677-681.
- A. McCallum, K. Nigam, and L. H. Ungar, "Efficient clustering of high-dimensional data sets with application to reference matching," in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, 2000, pp. 169-178.
- Y. Li, "Research on parallelization of clustering algorithm based on MapReduce," Sun Yat-Sen University, Guangzhou, China, 2010.
- J. C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters," Journal of Cybernetics, vol. 3, no. 3, pp. 32-57, 1973. https://doi.org/10.1080/01969727308546046
- J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algoritms, New York: Plenum Press, 1981.
- J. V. de Oliveira and W. Pedrycz, Advances in Fuzzy Clustering and Its Applications, New York: Wiley, 2007.
- M. Meloun, and J. Militky, Kompendium statistickeho zpracovani dat, Praha: Academia, 2006.
- E. H. Ruspini, "Numerical methods for fuzzy clustering," Information Sciences, vol. 2, no. 3, pp. 319-350, 1970. https://doi.org/10.1016/S0020-0255(70)80056-1
- A. Al-Dallal and R. S. Abdulwahab, "Achieving high recall and precision with HTLM documents: an innovation approach in information retrieval," in Proceedings of the World Congress on Engineering, London, 2011, pp. 1883-1888.
- J. Euzenat, "Semantic precision and recall for ontology alignment evaluation," in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, 2007, pp. 348-353.