A. K. Jain, M. N. Murty, and P. J. Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.264-323, 1999.
DOI : 10.1145/331499.331504

R. Xu and D. W. Ii, Survey of Clustering Algorithms, IEEE Transactions on Neural Networks, vol.16, issue.3
DOI : 10.1109/TNN.2005.845141

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

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

W. Stuetzle, Estimating the Cluster Tree of a Density by Analyzing the Minimal Spanning Tree of a Sample, Journal of Classification, vol.20, issue.1, pp.25-47, 2003.
DOI : 10.1007/s00357-003-0004-6

V. Olman, D. Xu, and Y. Xu, IDENTIFICATION OF REGULATORY BINDING SITES USING MINIMUM SPANNING TREES, Biocomputing 2003, pp.327-338, 2003.
DOI : 10.1142/9789812776303_0031

F. R. Bach and M. I. Jordan, Learning spectral clustering, with application to speech separation, Journal of Machine Learning Research, vol.7, pp.1963-2001, 2006.

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.

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.888-905, 2000.

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

G. H. Ball and D. J. Hall, ISODATA, a novel method of data analysis and classification, Tech. rep, 1965.

J. M. Peña, J. A. Lozano, and P. Larrañaga, An empirical comparison of four initialization methods for the K-Means algorithm, Pattern Recognition Letters, vol.20, issue.10, pp.1027-1040, 1999.
DOI : 10.1016/S0167-8655(99)00069-0

S. J. Redmond and C. Heneghan, A method for initialising the K-means clustering algorithm using kd-trees, Pattern Recognition Letters, vol.28, issue.8, pp.965-973, 2007.
DOI : 10.1016/j.patrec.2007.01.001

J. Rissanen, Stochastic Complexity in Statistical Inquiry, World Scientific Series in Computer Science, vol.15, 1989.
DOI : 10.1142/0822

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.411-423, 2001.
DOI : 10.1111/1467-9868.00293

J. Hartigan, Classification and Clustering, Journal of Marketing Research, vol.18, issue.4, 1975.
DOI : 10.2307/3151350

W. J. Krzanowski and Y. T. Lai, A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering, Biometrics, vol.44, issue.1, pp.23-34, 1985.
DOI : 10.2307/2531893

G. W. Milligan and M. C. Cooper, An examination of procedures for determining the number of clusters in a data set, Psychometrika, vol.77, issue.2, pp.159-179, 1985.
DOI : 10.1007/BF02294245

T. Calinski and J. Harabasz, A dendrite method for cluster analysis, Communications in statistics, vol.3, issue.1, pp.1-27, 1974.

H. Bischof, A. Leonardis, and A. Selb, MDL Principle for Robust Vector Quantisation, Pattern Analysis & Applications, vol.2, issue.1, pp.59-72, 1999.
DOI : 10.1007/s100440050015

D. Pelleg and A. Moore, X-means: Extending K-means with efficient estimation of the number of clusters, Proceedings of the 17th International Conference on Machine Learning, pp.727-734, 2000.

G. Hamerly and C. Elkan, Learning the k in k-means, Advances on 7th Annual Conference on Neural Information Processing Systems, pp.281-288, 2003.

E. Forgy, Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications, Biometrics, vol.21, pp.768-769, 1965.

D. Arthur and S. Vassilvitskii, k-means++: the advantages of careful seeding, Proceedings of the 18th annual ACM-SIAM symposium on Discrete algorithms (SODA), pp.1027-1035, 2007.

I. Katsavounidis, C. C. Kuo, and Z. Zhen, A new initialization technique for generalized Lloyd iteration, IEEE Signal Processing Letters, vol.1, issue.10, pp.144-146, 1994.
DOI : 10.1109/97.329844

L. Kaufman and P. J. Rousseeuw, Finding groups in data : an introduction to cluster analysis, Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics, 1990.
DOI : 10.1002/9780470316801

P. S. Bradley and U. M. Fayyad, Refining initial points for k-means clustering, Proceedings of 15th International Conference on Machine Learning, pp.91-99, 1998.

A. Likas, N. Vlassis, and J. J. Verbeek, The global k-means clustering algorithm, Pattern Recognition, vol.36, issue.2, pp.451-461, 2003.
DOI : 10.1016/S0031-3203(02)00060-2
URL : https://hal.archives-ouvertes.fr/inria-00321493

A. Badel, O. J. Michel, and A. O. Hero, Tree structured non linear signal modeling and prediction, IEEE trans. on Signal Processing, pp.47-58, 1999.

R. L. Graham and P. Hell, On the History of the Minimum Spanning Tree Problem, IEEE Annals of the History of Computing, vol.7, issue.1, pp.43-57, 1985.
DOI : 10.1109/MAHC.1985.10011

J. Gower and G. Ross, Minimum Spanning Trees and Single Linkage Cluster Analysis, Applied Statistics, vol.18, issue.1, pp.54-64, 1969.
DOI : 10.2307/2346439

J. R. Slagle, C. Chang, and R. C. Lee, Experiments with some cluster analysis algorithms, Pattern Recognition, vol.6, issue.3-4, pp.3-4, 1974.
DOI : 10.1016/0031-3203(74)90020-X

O. J. Michel, P. Bendjoya, and P. Rojoguer, Unsupervised clustering with MST: Application to astero¨?dastero¨?d data, Proceedings of 4th Physics in Signal and Images Processing, 2005.

J. M. Steele, Probability theory and combinatorial optimization, Society for Industrial and Applied Mathematics, vol.69, 1997.
DOI : 10.1137/1.9781611970029

J. H. Friedman, J. L. Bentley, and R. A. , An Algorithm for Finding Best Matches in Logarithmic Expected Time, ACM Transactions on Mathematical Software, vol.3, issue.3, pp.209-226, 1977.
DOI : 10.1145/355744.355745

J. L. Bentley and J. H. Friedman, Fast Algorithms for Constructing Minimal Spanning Trees in Coordinate Spaces, IEEE Transactions on Computers, vol.27, issue.2, pp.97-105, 1978.
DOI : 10.1109/TC.1978.1675043

A. Guttman, R-trees: A dynamic index structure for spatial searching, Proceedings of Annual Meeting SIGMOD, pp.47-57, 1984.

P. Yianilos, Data structures and algorithms for nearest neighbor search in general metric spaces, Proceedings of the 4th annual ACM- SIAM Symposium on Discrete algorithms, pp.311-321, 1993.

R. A. Finkel and J. L. Bentley, Quad trees a data structure for retrieval on composite keys, Acta Informatica, vol.4, issue.1, pp.1-9, 1974.
DOI : 10.1007/BF00288933

D. B. Lomet and B. Salzberg, The hB-tree: a multiattribute indexing method with good guaranteed performance, ACM Transactions on Database Systems, vol.15, issue.4, pp.625-658, 1990.
DOI : 10.1145/99935.99949

A. M. Mood, F. A. Graybill, and D. C. Boes, Introduction to the Theory of Statistics, 1974.

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

I. Csiszár, Information-type measures of difference of probability distributions and indirect observation, Studia Scientiarum Mathematicarum Hungarica, vol.2, pp.229-318, 1967.

L. M. Bregman, The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming, USSR Computational Mathematics and Mathematical Physics, vol.7, issue.3, pp.200-217, 1967.
DOI : 10.1016/0041-5553(67)90040-7

A. Hero, B. Ma, O. Michel, and J. Gorman, Applications of entropic spanning graphs, IEEE Signal Processing Magazine, vol.19, issue.5, pp.85-95, 2002.
DOI : 10.1109/MSP.2002.1028355

N. Keshava, Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.7, pp.1552-1565, 2004.
DOI : 10.1109/TGRS.2004.830549

I. S. Dhillon, S. Mallela, and R. Kumar, A divisive information-theoretic feature clustering algorithm for text classification, Journal of Machine Learning Research, vol.3, pp.1265-1287, 2003.

A. Banerjee, S. Merugu, I. S. Dhillon, and J. Gosh, Clustering with Bregman Divergences, Journal of Machine Learning Research, vol.6, pp.1705-1749, 2005.
DOI : 10.1137/1.9781611972740.22

D. L. Davies and D. W. Bouldin, A Cluster Separation Measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.1, issue.2, pp.224-227, 1979.
DOI : 10.1109/TPAMI.1979.4766909

F. Schmidt, Martian surface classification using omega hyperspectral images. spatiotemporal study of the co2 and h2o seasonal deposits, 2007.

F. Schmidt, S. Douté, and B. Schmitt, WAVANGLET: An Efficient Supervised Classifier for Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.5, pp.1374-1385, 2007.
DOI : 10.1109/TGRS.2006.890577
URL : https://hal.archives-ouvertes.fr/hal-00325458

S. Moussaoui, H. Hauksdóttir, F. Schmidt, C. Jutten, J. Chanussot et al., On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation, Neurocomputing, vol.71, issue.10-12, pp.2194-2208, 2008.
DOI : 10.1016/j.neucom.2007.07.034
URL : https://hal.archives-ouvertes.fr/hal-00272349

M. Belkin and P. Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Neural Computation, vol.15, issue.6, pp.1373-1396, 2003.
DOI : 10.1126/science.290.5500.2319