H. Bacelar-nicolau, The affinity coefficient in cluster analysis, pp.507-512, 1985.

H. Bacelar-nicolau, Analysis of symbolic data : exploratory methods for extracting statistical information from complex data, Ch. Similarity and Dissimilarity, pp.160-165, 2000.

T. Berners-lee, R. Fielding, and L. Masinter, Uniform Resource Identifiers (URI) : Generic Syntax, RFC, vol.2396, 1998.
DOI : 10.17487/rfc2396

H. H. Bock, Clustering algorithms and kohonen maps for symbolic data, Proc. of The International Conference on New Trends in Computational Statistics with Biomedical Applications (ICNCB), 2001.

H. H. Bock and E. Diday, Analysis of symbolic Data, Exploratory methods for extracting statistical information from complex data, 1999.

M. Chavent, F. A. De-carvalho, Y. Lechevallier, and R. Verde, Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle, pp.5-29, 2003.

M. Chavent and Y. Lechevallier, Dynamical Clustering of Interval Data: Optimization of an Adequacy Criterion Based on Hausdorff Distance, Classification, Clustering and Data Analysis, pp.53-60, 2002.
DOI : 10.1007/978-3-642-56181-8_5

Y. Cheng, Convergence and Ordering of Kohonen's Batch Map, Neural Computation, vol.9, issue.8, pp.1667-1676, 1997.
DOI : 10.1109/TPAMI.1984.4767478

M. Cottrell, J. Fort, and G. Pagès, Theoretical aspects of the SOM algorithm, Neurocomputing, vol.21, issue.1-3, pp.1-3, 1998.
DOI : 10.1016/S0925-2312(98)00034-4
URL : https://hal.archives-ouvertes.fr/hal-00141470

M. Cottrell, S. Ibbou, and P. Letrémy, SOM-based algorithms for qualitative variables, Neural Networks, vol.17, issue.8-9, pp.1149-1167, 2004.
DOI : 10.1016/j.neunet.2004.07.010
URL : https://hal.archives-ouvertes.fr/hal-00107960

M. Cottrell and P. Letrémy, How to use the Kohonen algorithm to simultaneously analyze individuals and modalities in a survey, Neurocomputing, vol.63, pp.193-207, 2005.
DOI : 10.1016/j.neucom.2004.04.011

A. Dereynì-es and . Septembre, Classification de données symboliques : une extension de la méthode des nuées dynamiques, Actes du IXème congrès de la société Francophone de Classification, pp.177-180, 2002.

A. Dereynì-es, Classification et discrimination en analyse de données symboliques, Thèse de doctorat, 2003.

E. Diday and G. Govaert, Classification automatique avec distances adaptatives, R.A.I.R.O. Informatique Computer Science, vol.11, issue.4, pp.329-349, 1977.

E. Diday and J. J. Simon, Clustering analysis, Digital Pattern Recognition, pp.47-94, 1976.
URL : https://hal.archives-ouvertes.fr/hal-00814810

E. Golli and A. , Extraction de données symboliques et cartes topologiques : Application aux données ayant une structure complexe, Thèse de doctorat, 2004.

E. Golli, A. Conan-guez, B. Rossi, and F. , Self organizing map and symbolic data, Journal of Symbolic Data Analysis, vol.2, issue.1, 2004.

J. Fort, M. Cottrell, and P. Letrémy, Stochastique on-line algorithm versus batch algorithm for quantization and self-organizing maps, Proceedings of Neural Networks for Signal Processing, 2001.

A. Foss, W. Wang, and O. R. Za¨?aneza¨?ane, A non-parametric approach to web log analysis, Proc. of Workshop on Web Mining in First International SIAM Conference on Data Mining (SDM2001), pp.41-50, 2001.

Y. Fu, K. Sandhu, and M. Shih, A Generalization-Based Approach to Clustering of Web Usage Sessions, Web Usage Analysis and User Profiling, pp.21-38, 2000.
DOI : 10.1007/3-540-44934-5_2

W. Gaul and L. Schmidt-thieme, Frequent generalized subsequences -a problem from web mining Data Analysis, Scientific Modelling and Practical Application, pp.429-445, 2000.

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

T. Graepel and K. Obermayer, A Stochastic Self-Organizing Map for Proximity Data, Neural Computation, vol.15, issue.1, pp.139-155, 1999.
DOI : 10.1109/18.144705

B. Hammer, A. Micheli, A. Sperduti, and M. Strickert, A general framework for unsupervised processing of structured data, Neurocomputing, vol.57, pp.3-35, 2004.
DOI : 10.1016/j.neucom.2004.01.008

T. Heskes and B. Kappen, Error potentials for self-organization, IEEE International Conference on Neural Networks, pp.1219-1223, 1993.
DOI : 10.1109/ICNN.1993.298731

H. Hotelling, Analysis of a complex of statistical variables into principal components., Journal of Educational Psychology, vol.24, issue.6, pp.417-441, 1933.
DOI : 10.1037/h0071325

T. Kohonen, Self-Organizing Maps, 3rd Edition, of Springer Series in Information Sciences, 1995.

T. Kohonen, Self-organizing maps of symbol strings, Neurocomputing, vol.21, issue.1-3, 1996.
DOI : 10.1016/S0925-2312(98)00031-9

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

T. Kohonen and P. J. Somervuo, How to make large self-organizing maps for nonvectorial data, Neural Networks, vol.15, issue.8-9, pp.945-952, 2002.
DOI : 10.1016/S0893-6080(02)00069-2

V. I. Levenshtein, Binary codes capable of correcting deletions, insertions and reversals, Sov. Phys. Dokl, vol.6, pp.707-710, 1966.

A. Luotonen, The common logfile format, 1995.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proc. of the Fifth Berkeley Symposium on Math, pp.281-297, 1965.

K. Matusita, Decision Rules, Based on the Distance, for Problems of Fit, Two Samples, and Estimation, The Annals of Mathematical Statistics, vol.26, issue.4, pp.1-30, 1951.
DOI : 10.1214/aoms/1177728422

K. Matusita, On the theory of statistical decision functions, Annals of the Institute of Statistical Mathematics, vol.3, issue.1, pp.631-640, 1955.
DOI : 10.1007/BF02949773

B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, Discovery and evaluation of aggregate usage profiles for web personalization, Data Mining and Knowledge Discovery, vol.6, issue.1, pp.61-82, 2002.
DOI : 10.1023/A:1013232803866

J. Ramsay and B. Silverman, Functional Data Analysis, 1997.

F. Rossi, B. Conan-guez, and A. Golli, Clustering functional data with the som algorithm, Proceedings of ESANN 2004, pp.305-312, 2004.

F. Rossi, B. Conan-guez, and A. Golli, Clustering and visualization of dissimilarity data with a fast self-organizing map, Tech. rep, 2005.

P. J. Somervuo, Online algorithm for the self-organizing map of symbol strings, Neural Networks, vol.17, issue.8-9, pp.1231-1239, 2004.
DOI : 10.1016/j.neunet.2004.08.004

J. Srivastava, R. Cooley, M. Deshpande, and P. Tan, Web usage mining, ACM SIGKDD Explorations Newsletter, vol.1, issue.2, pp.12-23, 2000.
DOI : 10.1145/846183.846188

D. Tanasa and B. Trousse, Le prétraitement des fichiers log web dans le web usage mining multi-sites, 2003.

D. Tanasa and B. Trousse, Advanced data preprocessing for intersites Web usage mining, IEEE Intelligent Systems, vol.19, issue.2, pp.59-65, 2004.
DOI : 10.1109/MIS.2004.1274912
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.98.3832

D. Tanasa and B. Trousse, Data preprocessing for WUM, IEEE Potentials, vol.23, issue.3, pp.22-25, 2004.
DOI : 10.1109/MP.2004.1341781

J. B. Tenenbaum, V. De-silva, and J. C. 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

W. S. Torgerson, Multidimensional scaling: I. Theory and method, Psychometrika, vol.3, issue.4, pp.401-419, 1952.
DOI : 10.1007/BF02288916

R. Verde, F. De-carvalho, and Y. Lechevallier, A Dynamical Clustering Algorithm for Multi-nominal Data, Data Analysis, Classification, and Related Method, pp.387-394, 2000.
DOI : 10.1007/978-3-642-59789-3_61

W. Html and W. Group, XHTML 1.0 the Extensible HyperText Markup Language. W3C recommandation, W3C, second Edition, 2002.

J. Wang, X. Wang, K. Lin, D. Shasha, B. A. Shapiro et al., Evaluating a class of distance-mapping algorithms for data mining and clustering, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.307-311, 1999.
DOI : 10.1145/312129.312264