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Issue No.06 - June (2010 vol.22)
pp: 900-905
Daewon Lee , University of Ulsan, Ulsan
Jaewook Lee , Pohang University of Science and Technology, Pohang
Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space. Simulation results demonstrate that clustering based on the proposed dissimilarity measure is robust to the choice of kernel parameters and able to control the number of clusters efficiently.
Clustering, kernel methods, dynamical systems, equilibrium vector, support.
Daewon Lee, Jaewook Lee, "Dynamic Dissimilarity Measure for Support-Based Clustering", IEEE Transactions on Knowledge & Data Engineering, vol.22, no. 6, pp. 900-905, June 2010, doi:10.1109/TKDE.2009.140
[1] A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik, "Support Vector Clustering," J. Machine Learning Research, vol. 2, pp. 125-137, 2001.
[2] F. Camastra and A. Verri, "A Novel Kernel Method for Clustering," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 801-805, May 2005.
[3] T. Ban and S. Abe, "Spatially Chunking Support Vector Clustering Algorithm," Proc. Int'l Joint Conf. Neural Networks, pp. 414-418, 2004.
[4] J. Lee and D. Lee, "An Improved Cluster Labeling Method for Support Vector Clustering," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 461-464, Mar. 2005.
[5] M. Girolami, "Mercer Kernel-Based Clustering in Feature Space," IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 780-784, May 2002.
[6] S. Chen and D. Zhang, "Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-Induced Distance Metric," IEEE Trans. System, Man, and Cybernetics—Part B, vol. 34, no. 4, pp. 1907-1916, Aug. 2004.
[7] D. Zhang and S. Chen, "A Novel Kernelised Fuzzy C-Means Algorithm with Application in Medical Image Segmentation," Artificial Intelligence in Medicine, vol. 32, no. 1, pp. 37-50, 2004.
[8] D.M.J. Tax and R.P.W. Duin, "Support Vector Domain Description," Pattern Recognition Letters, vol. 20, pp. 1191-1199, 1999.
[9] B. Schölkopf, J. Platt, J. Shawe-Taylor, A. Smola, and R. Williamson, "Estimating the Support of a High-Dimensional Distribution," Neural Computation, vol. 13, no. 7, pp. 1443-1471, 2001.
[10] J. Yang, V. Estivill-Castro, and S.K. Chalup, "Support Vector Clustering through Proximity Graph Modelling," Proc. Ninth Int'l Conf. Neural Information Processing (ICONIP '02), pp. 898-903, 2002.
[11] J. Park, X. Ji, H. Zha, and R. Kasturi, "Support Vector Clustering Combined with Spectral Graph Partitioning," Proc. 17th Int'l Conf. Pattern Recognition (ICPR04), pp. 581-584, 2004.
[12] W.J. Puma-Villanueva, G.B. Bezerra, C.A.M. Lima, and F.J.V. Zuben, "Improving Support Vector Clustering with Ensembles," Proc. Int'l Joint Conf. Neural Networks, 2005.
[13] M.S. Hansen, K. Sjöstrand, H. Ólafsdóttir, H.B. Larsson, M.B. Stegmann, and R. Larsen, "Robust Pseudohierarchical Support Vector Clustering," Proc. Scandinavian Conf. Image Analysis (SCIA '07), pp. 808-817, 2007.
[14] J. Lee and D. Lee, "Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 461-464, Nov. 2006.
[15] H.-C. Kim and J. Lee, "Clustering Based on Gaussian Processes," Neural Computation, vol. 19, no. 11, pp. 3088-3107, 2007.
[16] D. Lee and J. Lee, "Equilibrium-Based Support Vector Machine for Semi-Supervised Classification," IEEE Trans. Neural Networks, vol. 18, no. 2, pp. 578-583, Mar. 2007.
[17] D. Lee and J. Lee, "Domain Described Support Vector Classifier for Multi-Classification Problems," Pattern Recognition, vol. 40, pp. 41-51, 2007.
[18] J. Guckenheimer and P. Homes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. Springer, 1986.
[19] H.K. Khalil, Nonlinear Systems. Macmillan, 1992.
[20] J. Lee, "An Optimization-Driven Framework for the Computation of the Controlling UEP in Transient Stablity Analysis," IEEE Trans. Automatic Control, vol. 49, no. 1, pp. 115-119, Jan. 2004.
[21] J. Lee, "A Novel Three-Phase Trajectory Informed Search Methodology for Global Optimization," J. Global Optimization, vol. 38, no. 1, pp. 61-77, 2007.
[22] J.B. Kruskal, "On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem," Proc. Am. Math. Soc., vol. 7, no. 1, pp. 48-50, 1956.
[23] A.Y. Ng, M.I. Jordan, and Y. Weiss, "On Spectral Clustering: Analysis and an Algorithm," Advances in Neural Information Processing Systems, pp. 849-856, MIT Press, 2001.
[24] L. Hubert and P. Arabie, "Comparing Partitions," J. Classification, vol. 2, pp. 193-218, 1985.
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