An improved spectral clustering algorithm based on random walk
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Ng A Y, Jordan M I, Weiss Y. On spectral clustering: analysis and an algorithm. In: Proceedings of Advances in Neural Information Pressing Systems 14. 2001, 849–856
Wang F, Zhang C S, Shen H C, Wang J D. Semi-supervised classification using linear neighborhood propagation. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006, 160–167
Wang F, Zhang C S. Robust self-tuning semi-supervised learning. Neurocomputing, 2006, 70(16–18): 2931–2939
Kamvar S D, Klein D, Manning C D. Spectral learning. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence. 2003, 561–566
Lu Z D, Carreira-Perpiňán M A. Constrained spectral clustering through affinity propagation. In: Proceedings of 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2008, 1–8
Meila M, Shi J. A random walks view of spectral segmentation. In: Proceedings of 8th International Workshop on Artificial Intelligence and Statistics. 2001
Azran A, Ghahramani Z. Spectral methods for automatic multiscale data clustering. In: Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006, 190–197
Meila M. The multicut lemma.UW Statistics Technical Report 417, 2001
Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888–905
Hagen L, Kahng A B. New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1992, 11(9): 1074–1085
Ding C H Q, He X F, Zha H Y, Gu M, Simon H D. A min-max cut algorithm for graph partitioning and data clustering. In: Proceedings of 1st IEEE International Conference on Data Mining. 2001, 107–114
Zelnik-Manor L, Perona P. Self-tuning spectral clustering. In. Proceedings of Advances in Neural Information Processing Systems 17. 2004, 1601–1608
Huang T, Yang C. Matrix Analysis with Applications. Beijing: Scientific Publishing House, 2007 (in Chinese)
Lovász L, Lov L, Erdos O. Random walks on graphs: a survey. Combinatorics, 1993, 2: 353–398
Gong C H. Matrix Theory and Applications. Beijing: Scientific Publishing House, 2007 (in Chinese)
Tian Z, Li X B, Ju Y W. Spectral clustering based on matrix perturbation theory. Science in China Series F: Information Sciences, 2007, 50(1): 63–81
Fouss F, Pirotte A, Renders J, Saerens M. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(3): 355–369
Banerjee A, Dhillon I, Ghosh J, Sra S. Generative model-based clustering of directional data. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003, 19–28
Wang L, Leckie C, Ramamohanarao K, Bezdek J C. Approximate spectral clustering. In: Proceedings of 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 2009, 134–146
Fowlkes C, Belongie S, Chung F, Malik J. Spectral grouping using the Nyström method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2): 214–225
Puzicha J, Belongie S. Model-based halftoning for color image segmentation. In: Proceedings of 15th International Conference on Pattern Recognition. 2000, 629–632