Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map

Bache, K. and Lichman, M. (2013). UCI Machine Learning Repository, University of California, Irvine, CA.Search in Google Scholar

Berkhin, P. (2002). Survey of clustering data mining techniques, Technical report, Accrue Software, https://www.cc.gatech.edu/isbell/reading/papers/berkhin02survey.pdf.Search in Google Scholar

Bezdek, J.C., Ehrlich, R. and Full, W. (1984). FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences 10(2-3): 191-203.10.1016/0098-3004(84)90020-7Search in Google Scholar

Bowden, G.J., Dandy, G.C. and Maier, H.R. (2005). Input determination for neural network models in water resources applications. Part 1-Background and methodology, Journal of Hydrology 301(1): 75-92.10.1016/j.jhydrol.2004.06.021Search in Google Scholar

Cali´nski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis, Communications in Statistics-Theory and Methods 3(1): 1-27.10.1080/03610927408827101Search in Google Scholar

Carrasco Kind, M. and Brunner, R.J. (2014). SOMs: Photometric redshift PDFs with self-organizing maps and random atlas, Monthly Notices of the Royal Astronomical Society 438(4): 3409-3421.10.1093/mnras/stt2456Search in Google Scholar

Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification, IEEE Transactions on Information Theory 13(1): 21-27.10.1109/TIT.1967.1053964Search in Google Scholar

Davies, D.L. and Bouldin, D.W. (1979). A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2): 224-227.10.1109/TPAMI.1979.4766909Search in Google Scholar

Hamzacebi, C. (2008). Improving genetic algorithms performance by local search for continuous function optimization, Journal of Applied Mathematics and Computation 196(1): 309-317.10.1016/j.amc.2007.05.068Search in Google Scholar

Harkanth, S. and Phulpagar, B.D. (2013). A survey on clustering methods and algorithms, International Journal of Computer Science and Information Technologies 4(5): 687-691.Search in Google Scholar

Hartigan, J.A. and Wong, M.A. (1979). Algorithm AS 136: A k-means clustering algorithm, Journal of the Royal Statistical Society C: Applied Statistics 28(1): 100-108.10.2307/2346830Search in Google Scholar

Husain, H., Khalid, M. and R., Y. (2004). Automatic clustering of generalized regression neural network by similarity index based fuzzy c-means clustering, IEEE Region 10 Conference, Chiang Mai, Thailand, pp. 302-305.10.1109/TENCON.2004.1414591Search in Google Scholar

Jain, A.K., Mao, J. and Mohiuddin, K.M. (1998). Artificial neural networks: A tutorial, IEEE Computer 29(3): 31-44.10.1109/2.485891Search in Google Scholar

Kohonen, T. (1982). Self-organized formation of topologically correct feature maps, Biological Cybernetics 43(1): 59-69.10.1007/BF00337288Search in Google Scholar

Kokkinos, Y. and Margaritis, K.G. (2015). A fast progressive local learning regression ensemble of generalized regression neural networks, Proceedings of the 19th Panhellenic Conference on Informatics, Athens, Greece, pp. 109-114 .10.1145/2801948.2801962Search in Google Scholar

Kolesnikov, A., Trichina, E. and Kauranne, T. (2015). Estimating the number of clusters in a numerical data set via quantization error modeling, Pattern Recognition 48(3): 941-952.10.1016/j.patcog.2014.09.017Search in Google Scholar

Kotsiantis, S.B. and Pintelas, P.E. (2004). Recent advances in clustering: A brief survey, WSEAS Transactions on Information Science and Applications 1(1): 73-81.Search in Google Scholar

Krenker, A., Bester, J. and Kos, A. (2011). Introduction to the artificial neural networks, in K. Suzuki (Ed.), Artificial Neural Networks-Methodological Advances and Biomedical Applications, Intech, Rijeka, pp. 3-18.10.5772/15751Search in Google Scholar

Maier, H. and Dandy, G. (1997). Determining inputs for neural network models of multivariate time series, Microcomputers in Civil Engineering 12(5): 353368.10.1111/0885-9507.00069Search in Google Scholar

Rama, B., Jayashree, P. and Jiwani, S. (2010). A survey on clustering, current status and challenging issues, International Journal on Computer Science and Engineering 2(9): 2976-2980.Search in Google Scholar

Rousseeuw, P.J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 20: 53-65.10.1016/0377-0427(87)90125-7Search in Google Scholar

Sabo, K. (2014). Center-based l1-clustering method, International Journal of Applied Mathematics and Computer Science 24(1): 151-163, DOI: 10.2478/amcs-2014-0012.10.2478/amcs-2014-0012Open DOISearch in Google Scholar

Specht, D.F. (1991). A general regression neural network, IEEE Transactions on Neural Networks 2(6): 568-576.10.1109/72.9793418282872Search in Google Scholar

Szemenyei, M. and Vajda, F. (2017). Dimension reduction for objects composed of vector sets, International Journal of Applied Mathematics and Computer Science 27(1): 169-180, DOI: 10.1515/amcs-2017-0012.10.1515/amcs-2017-0012Open DOISearch in Google Scholar

Tang, K., Li, X., Suganthan, P.N., Yang, Z. andWeise, T. (2009). Benchmark functions for the CEC’2010 special session and competition on large scale global optimization, Technical report, Nature Inspired Computation and Applications Laboratory, USTC, Hefei.Search in Google Scholar

Tibshirani, R., Walther, G. and Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society B: Statistical Methodology 63(2): 411-423.10.1111/1467-9868.00293Search in Google Scholar

Yuen, R.K.K., Lee, E.W.M., Lim, C.P. and Cheng, G.W.Y. (2004). Fusion of GRNN and FA for online noisy data regression, Neural Processing Letters (19): 227-241.10.1023/B:NEPL.0000035614.53039.c3Search in Google Scholar

Zhao, S.J., Zhang, J.L., Li, X. and Song,W. (2007). Generalized regression neural network based on fuzzy means clustering and its application in system identification, Proceedings of the International Symposium on Information Technology Convergence, Joenju, South Korea, pp. 13-16.10.1109/ISITC.2007.57Search in Google Scholar

Zheng, L.G., Yu, M.G., Yu, S.J. andWang,W. (2008). Improved prediction of nitrogen oxides using GRNN with k-means clustering and EDA, Proceedings of the 4th International Conference on Natural Computation, Jinan, China, pp.91-95.10.1109/ICNC.2008.478Search in Google Scholar