Y. Chen, C. D. Jensen, E. Gray, and J. M. Seigneur, Risk probability estimating based on clustering, IEEE Systems, Man and Cybernetics SocietyInformation Assurance Workshop, 2003., 2003.
DOI : 10.1109/SMCSIA.2003.1232426

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5652

D. Cvetkovi´ccvetkovi´c, Signless Laplacians and line graphs, Bull. Acad. Serbe Sci. Arts, Cl. Sci. Math. Natur., Sci. Math, vol.131, issue.30, pp.85-92, 2005.

V. Deepak and M. Meila, Comparison of Spectral Clustering Methods, 2003.

Y. Elon, Eigenvectors of the discrete Laplacian on regular graphs???a statistical approach, Journal of Physics A: Mathematical and Theoretical, vol.41, issue.43, p.41, 2008.
DOI : 10.1088/1751-8113/41/43/435203

I. Fischer and J. Poland, Amplifying the Block Matrix Structure for Spectral Clustering, 2005.

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

A. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, vol.31, issue.8, pp.651-666, 2010.
DOI : 10.1016/j.patrec.2009.09.011

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.4286

L. Macqueen, Some methods for classification and analysis of multivariate observations, 5th Berkeley Symposium on Mathematical Statistics and Probabilitz, pp.281-297, 1967.

M. Maier, M. Hein, V. Luxburg, and U. , Cluster Identification in Nearest-Neighbor Graphs, Proc. of the 18th International Conference on Algorithmic Learning Theory, ALT'07, pp.196-210, 2007.
DOI : 10.1109/TMC.2003.1195149

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.8150

M. Meila and J. Shi, A random walks view of spectral segmentation, Proc. of 10th International Workshop on Artificial Intelligence and Statistics (AISTATS), pp.8-11, 2001.

M. E. Newman, Detecting community structure in networks. European Physics, J. B, vol.38, pp.321-330, 2004.
DOI : 10.1140/epjb/e2004-00124-y

A. Ng, M. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, Advances in Neural Information Processing Systems 14, pp.849-856, 2001.

M. Sanchez-silva, Applicability of Network Clustering Methods for Risk Analysis, Soft Computing in Civil and Structural Engineering, pp.283-306, 2009.
DOI : 10.4203/csets.23.11

T. Shi, M. Belkin, Y. , and B. , Data spectroscopy: Eigenspaces of convolution operators and clustering, The Annals of Statistics, vol.37, issue.6B, pp.3960-3984, 2009.
DOI : 10.1214/09-AOS700

URL : http://arxiv.org/abs/0807.3719

U. Von-luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1017/CBO9780511810633

T. Xia, J. Cao, Y. Zhang, L. , and J. , On defining affinity graph for spectral clustering through ranking on manifolds, Neurocomputing, vol.72, issue.13-15, pp.3203-3211, 1315.
DOI : 10.1016/j.neucom.2009.03.012

R. Xu, I. Wunsch, and D. , 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, Proc. of NIPS'04, pp.1601-1608, 2004.

J. Zhang, A Clustering Application in Portfolio Management, Lecture Notes in Electrical Engineering, vol.60, pp.309-321, 2010.
DOI : 10.1007/978-90-481-8776-8_27