{"references": ["", "J. Han and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed.\nSan Francisco: Morgan Kaufmann, 2006, ch. 7, pp. 308\u2013466.", "J. A. Hartigan and M. A. Wong, \"A K-Means Clustering Algorithm,\" J.\nROY. STAT. SOC. C-APP., pp. 100\u2013108, 1979.", "M. J. Brusco and H. F. K\u00a8ohn, \"Optimal partitioning of a data set based\non the P-median model,\" Psychometrika., vol. 73, pp. 89\u2013105, Murch\n2008.", "M. Ester, H. P. Kriegel, J. Sander, and X. Xu, \"A density-based algorithm\nfor discovering clusters in large spatial databases with noise,\" Proceedings\nof Second International Conference on Knowledge Discovery and\nData Mining, Portland, pp. 226\u2013231, 1996.", "D. Horn, A. BenHur, H. Siegelmann, and V. Vapnik, \"A support\nvector method for clustering,\" in Advances in Neural Information\nProcessingSystems, ser. Proceedings of the 2000 Conference, T. Leen,\nT. Dietterich, and V. Tresp, Eds., vol. 13. MIT Press, 2001, pp. 367\u2013\n373.", "D. Horn, \"Clustering via hilbert space,\" Physica A, vol. 302, pp. 70\u201379,\n2001.", "L. Ping, Z. Chun-Guang, and Z. Xu, \"Improved support vector clustering,\"\nENG. APPL. ARTIF. INTEL., vol. 23, pp. 552\u2013559, 2010.", "Y. Sun, Y. Wang, J. Wang, W. Du, and C. Zhou, \"A novel svc method\nbased on k-means,\" in Second International Conference on Future\nGeneration Communication and Networking, 2008, pp. 55\u201358.", "J. Lee and D. Lee, \"An improved cluster labeling method for support\nvector clustering,\" IEEE. T. PATTERN. ANAL., pp. 461\u2013464, 2005.\n[10] M. D. Buhmann, Radial Basis Functions: Theory and Implementations.\nNew York: Cambridge University Press, 2004.\n[11] C. Aggarwal, C. Procopiuc, J. Wolf, P. S. Yu, and J. S. Park, \"Fast\nalgorithms for projected clustering,\" in In Proc. 1999 ACM-SIGMOD,\nInt. Conf. Management of Data (SIGMOD99), philadelphia, PA, June\n1999, pp. 61\u201372.\n[12] R. Aggrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, \"Automatic\nsubspace clustering of high dimensional data for data mining applications,\"\nin Management of Data (SIGMOD98).\n[13] S. Russel and P. Norving, Artificial Intelligence, A modern approach\n(pages 32-57), 2nd ed. Prentice Hall, 2003.\n[14] R. A. Fisher, \"The use of multiple measurments in taxonomic problems,\"\nAnnals of Eugenics, pp. 179\u2013188, 1936.\n[15] R. Fletcher, Practical Methods of Optimization. Chichester: Wiley-\nInterscience, 1987.\n[16] M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.\nNew York: Dover Publications, 1972.\n[17] T. Zheng, L. Xiaobin, and J. Yanwei, \"Disturbing analysis on spectrum\nclustering,\" Science in China(SeriesE), vol. 37, no. 4, pp. 527\u2013543, 2007.\n[18] www.maplesoft.com.\n[19] A. BenHur, D. Horn, H. Siegelmann, and V. Vapnik, \"Support vector\nclustering,\" Machine Learning Research, vol. 2, pp. 125\u2013137, 2001.\n[20] A. J. Khattak, S. I. A.Tirmizi, and S. U. Islam, \"Application of meshfree\ncollocation method to a class of nonlinear partial differential equations,\"\nENG. ANAL. BOUND. ELEM., vol. 33, pp. 661\u2013667, 2009.\n[21] M. Dehghan and A. Shokri, \"A meshless method for numerical solution\nof the one-dimensional wave equation with an integral condition using\nradial basis functions,\" Numer. Algorithms., vol. 52, pp. 461\u2013477, 2009.\n[22] \"Numerical solution of the nonlinear Klein-Gordon equation using\nradial basis functions,\" J. Comput. Appl. Math., vol. 230, pp. 400\u2013410,\n2009.\n[23] S. U. Islam, S. Haqb, and A. Ali, \"A meshfree method for the numerical\nsolution of the RLW equation,\" J. Comput. Appl. Math., vol. 223, pp.\n997\u20131012, 2009.\n[24] M. D. Buhmann, \"Radial basis functions,\" Acta Numerica, vol. 16, pp.\n1\u201338, 2000.\n[25] S. Sarra, \"Adaptive radial basis function method for time dependent\npartial differential equations,\" Appl. Numer. Math., vol. 54, pp. 79\u201394,\n2005.\n[26] H. Wendland, Scattered Data Approximation. New York: Cambridge\nUniversity Press, 2005."]}

Support vector clustering (SVC) is an important kernelbased clustering algorithm in multi applications. It has got two main bottle necks, the high computation price and labeling piece. In this paper, we presented a modified SVC method, named Grid–SVC, to improve the original algorithm computationally. First we normalized and then we parted the interval, where the SVC is processing, using a novel Grid–based clustering algorithm. The algorithm parts the intervals, based on the density function of the data set and then applying the cartesian multiply makes multi-dimensional grids. Eliminating many outliers and noise in the preprocess, we apply an improved SVC method to each parted grid in a parallel way. The experimental results show both improvement in time complexity order and the accuracy.