A. Asuncion and D. J. Newman, UCI machine learning repository, 2007.

M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, Advances on 15th Annual Conference on Neural Information Processing Systems, 2001.

C. Chang, An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis, IEEE Transactions on Information Theory, vol.46, issue.5, pp.1927-1932, 2000.
DOI : 10.1109/18.857802

F. R. Chung, Spectral Graph Theory, Number 92 in Conference Board on the Mathematical Sciences, 1997.
DOI : 10.1090/cbms/092

J. Costa and A. O. Hero, Geodesic Entropic Graphs for Dimension and Entropy Estimation in Manifold Learning, IEEE Transactions on Signal Processing, vol.52, issue.8, pp.2210-2221, 2004.
DOI : 10.1109/TSP.2004.831130

L. Galluccio, O. Michel, P. Comon, M. Kliger, and A. O. Hero, Dual rooted trees based clustering, Laboratoire, vol.3, 2010.

S. Grikschat, J. A. Costa, A. O. Hero, and O. Michel, Dual Rooted-Diffusions for Clustering and Classification on Manifolds, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006.
DOI : 10.1109/ICASSP.2006.1661431

S. Lafon, Y. Keller, and R. R. Coifman, Data Fusion and Multicue Data Matching by Diffusion Maps, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.11, pp.1784-1797, 2006.
DOI : 10.1109/TPAMI.2006.223

U. and V. Luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1007/s11222-007-9033-z

J. B. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, pp.281-287, 1967.

A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, Advances on 15th Annual Conference on Neural Information Processing Systems, 2001.

R. Prim, Shortest Connection Networks And Some Generalizations, Bell System Technical Journal, vol.36, issue.6, pp.1389-1401, 1957.
DOI : 10.1002/j.1538-7305.1957.tb01515.x

A. Schclar, A Diffusion Framework for Dimensionality Reduction, Soft Computing for Knowledge Discovery and Data Mining, pp.315-325, 2008.
DOI : 10.1007/978-0-387-69935-6_13

J. Tenenbaum, V. De-silva, and J. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000.
DOI : 10.1126/science.290.5500.2319

S. Theodoridis and K. Koutroumbas, Pattern Recognition, 2006.
DOI : 10.1016/B0-12-227240-4/00132-5

W. S. Torgerson, Theory and methods of scaling, 1958.

R. Xu, D. Wunsch, and I. , Survey of Clustering Algorithms, IEEE Transactions on Neural Networks, vol.16, issue.3, pp.645-678, 2005.
DOI : 10.1109/TNN.2005.845141

L. Zelnik-manor and P. Perona, Self-tuning spectral clustering, Advances in Neural Information Processing Systems 17, pp.1601-1608, 2005.