T. Kohonen and P. J. Somervuo, Self-organizing maps of symbol strings, Neurocomputing, vol.21, issue.1-3, pp.19-30, 1998.
DOI : 10.1016/S0925-2312(98)00031-9

B. Conan-guez, F. Rossi, and A. Golli, Fast algorithm and implementation of dissimilarity self-organizing maps, Neural Networks, vol.19, issue.6-7, pp.6-7855, 2006.
DOI : 10.1016/j.neunet.2006.05.002
URL : https://hal.archives-ouvertes.fr/inria-00174196

T. Graepel, M. Burger, and K. Obermayer, Self-organizing maps: Generalizations and new optimization techniques, Neurocomputing, vol.21, issue.1-3, pp.173-190, 1998.
DOI : 10.1016/S0925-2312(98)00035-6
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.298

D. , M. Donald, and C. Fyfe, The kernel self organising map, Proceedings of 4th International Conference on knowledge-based intelligence engineering systems and applied technologies, pp.317-320, 2000.

K. W. Lau, H. Yin, and S. Hubbard, Kernel self-organising maps for classification, Neurocomputing, vol.69, issue.16-18, pp.2033-2040, 2006.
DOI : 10.1016/j.neucom.2005.10.003

B. Hammer and A. Hasenfuss, Topographic Mapping of Large Dissimilarity Data Sets, Neural Computation, vol.2005, issue.9, pp.2229-2284, 2010.
DOI : 10.1162/jmlr.2003.4.6.1001

M. Olteanu and N. Villa-vialaneix, On-line relational and multiple relational SOM, Neurocomputing, vol.147, pp.15-30, 2015.
DOI : 10.1016/j.neucom.2013.11.047
URL : https://hal.archives-ouvertes.fr/hal-01063831

F. Rossi, How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?, Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014), volume 295 of Advances in Intelligent Systems and Computing, pp.3-23, 2014.
DOI : 10.1007/978-3-319-07695-9_1
URL : https://hal.archives-ouvertes.fr/hal-01017468

N. Aronszajn, Theory of reproducing kernels. Transactions of the, pp.337-404, 1950.
DOI : 10.2307/1990404
URL : http://www.dtic.mil/get-tr-doc/pdf?AD=ADA296533

M. Olteanu, N. Villa-vialaneix, and M. Cottrell, On-Line Relational SOM for Dissimilarity Data, Advances in Self-Organizing Maps (Proceedings of WSOM 2012Advances in Intelligent Systems and Computing), pp.13-22
DOI : 10.1007/978-3-642-35230-0_2
URL : https://hal.archives-ouvertes.fr/hal-00768948

J. Mariette, M. Olteanu, and N. Villa-vialaneix, Efficient interpretable variants of online SOM for large dissimilarity data, Neurocomputing, vol.225, pp.31-48, 2017.
DOI : 10.1016/j.neucom.2016.11.014
URL : https://hal.archives-ouvertes.fr/hal-01465340

L. Goldfarb, A unified approach to pattern recognition, Pattern Recognition, vol.17, issue.5, pp.575-582, 1984.
DOI : 10.1016/0031-3203(84)90056-6

L. A. Adamic and N. Glance, The political blogosphere and the 2004 us election: divided they blog, Proceedings of the 3rd LINKDD Workshop, pp.36-43, 2005.
DOI : 10.1145/1134271.1134277

C. P. Meyer and G. Paulay, DNA Barcoding: Error Rates Based on Comprehensive Sampling, PLoS Biology, vol.85, issue.12, 2005.
DOI : 10.1371/journal.pbio.0030422.st003
URL : http://doi.org/10.1371/journal.pbio.0030422

M. Kimura, A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences, Journal of Molecular Evolution, vol.206, issue.5, Nov., pp.111-120, 1980.
DOI : 10.1007/BF01731581

P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, Modeling wine preferences by data mining from physicochemical properties, Decision Support Systems, vol.47, issue.4, pp.547-553, 2009.
DOI : 10.1016/j.dss.2009.05.016