R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high dimensional data, 2005.

M. A. Cox and T. F. Cox, Multidimensional scaling, Handbook of Data Visualization, pp.315-347, 2008.

H. Kriegel, P. Kro3ger, and A. Zimek, Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering, ACM Transactions on Knowledge Discovery from Data, vol.3, issue.1, pp.1-58, 2009.

N. G. Pavlidis, D. P. Hofmeyr, and S. K. Tasoulis, Minimum density hyperplanes, Journal of Machine Learning Research, vol.17, issue.156, pp.1-33, 2016.

B. Schoelkopf, A. Smola, and K. Mueller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation, vol.10, issue.5, pp.1299-1319, 1998.

S. K. Tasoulis, D. K. Tasoulis, and V. P. Plagianakos, Enhancing principal direction divisive clustering, Pattern Recognition, vol.43, issue.10, pp.3391-3411, 2010.

J. B. Tenenbaum, V. Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000.

H. Yu, X. Zhang, Y. Yang, X. Zhao, and L. Cai, An extended isomap by enhancing similarity for clustering, Advanced Research in Applied Arti_cial Intelligence, vol.7345, pp.808-815, 2012.

M. Gonen and A. A. Margolin, Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology, Advances in Neural Information Processing Systems 27 (NIPS 2014), 2014.

M. Homepage,

L. Ulrike-von, A Tutorial on Spectral Clustering, Statistics and Computing, vol.17, issue.4, p.2007