Spectral Clustering Algorithms

Implementation of four key algorithms of Spectral Graph Clustering using eigen vectors : Tutorial

The code for the spectral graph clustering concepts presented in the following papers is implemented for tutorial purpose:
1. Ng, A., Jordan, M., and Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. In T. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 849 – 856). MIT Press.

2. P. Perona and W. T. Freeman, "A factorization approach to grouping",In H. Burkardt and B. Neumann, editors, Proc ECCV, pages 655-670, 1998.

3. J. Shi and J. Malik, "Normalized Cuts and Image Segmentation", In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 731-737, 1997.

4. G.L. Scott and H. C. Longuet-Higgins, "Feature Grouping by Relocalisation of Eigenvectors of the Proxmity Matrix", In Proc. British Machine Vision Conference, pages 103-108, 1990.

Evolution of spectral clustering methods and the various concepts proposed by the above authors are demonstrated in this implementation.