Une adaptation des cartes auto-organisatrices pour des données décrites par un tableau de dissimilarités
El Golli, Aïcha ; Rossi, Fabrice ; Conan-Guez, Brieuc ; Lechevallier, Yves
Revue de Statistique Appliquée, Tome 54 (2006), p. 33-64 / Harvested from Numdam
Publié le : 2006-01-01
@article{RSA_2006__54_3_33_0,
     author = {El Golli, A\"\i cha and Rossi, Fabrice and Conan-Guez, Brieuc and Lechevallier, Yves},
     title = {Une adaptation des cartes auto-organisatrices pour des donn\'ees d\'ecrites par un tableau de dissimilarit\'es},
     journal = {Revue de Statistique Appliqu\'ee},
     volume = {54},
     year = {2006},
     pages = {33-64},
     language = {fr},
     url = {http://dml.mathdoc.fr/item/RSA_2006__54_3_33_0}
}
El Golli, Aïcha; Rossi, Fabrice; Conan-Guez, Brieuc; Lechevallier, Yves. Une adaptation des cartes auto-organisatrices pour des données décrites par un tableau de dissimilarités. Revue de Statistique Appliquée, Tome 54 (2006) pp. 33-64. http://gdmltest.u-ga.fr/item/RSA_2006__54_3_33_0/

Bacelar-Nicolau H. ( 1985), The affinity coefficient in cluster analysis. Methods of operations research, 53, 507-512. | Zbl 0594.62071

Bacelar-Nicolau H. ( 2000), Analysis of symbolic data : exploratory methods for extracting statistical information from complex data, H. H. Bock and E. Diday, Ch. Similarity and Dissimilarity, pp. 160-165. | MR 1792132 | Zbl 0977.62066

Berners-Lee T., Fielding R., Masinter L. (August 1998), Uniform Resource Identifiers (URI) : Generic Syntax. RFC 2396, The Internet Society, http://www.ietf.org/rfc/rfc2396.txt.

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

Bock H. H., Diday E. ( 1999), Analysis of symbolic Data, Exploratory methods for extracting statistical information from complex data, Springer. | MR 1792132 | Zbl 1039.62501

Celeux G., Diday E., Govaert G., Lechevallier Y., Ralambondrainy H. ( 1989), Classification automatique des données, Dunod informatique.

Chavent M., De Carvalho F.A.T., Lechevallier Y., Verde R. ( 2003), Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle, Revue de Statistique Appliquées, 4, 5-29. | Numdam

Chavent M., Lechevallier Y. ( 2002), Dynamical clustering of interval data. Optimization of an adequacy criterion based on Hausdorff distance, in Jajuga K., Sokolowski A. et Bock H.H. (Eds.) : Classification, Clustering and Data Analysis, Springer, pp. 53-60. | MR 2010438 | Zbl 1032.62058

Cheng Y. (November 1997), Convergence and ordering of Kohonen's batch map, Neural Computation, 9 (8), 1667-1676.

Conan-Guez B., Rossi F., El Golli A. ( 2006), Fast algorithm and implementation of dissimilarity self-organizing maps, Neural Networks, 19 (6-7), 855-863. | Zbl 1102.68540

Cottrell M., Fort J.-C., Pagès G. (November 1998), Theoretical aspects of the SOM algorithm, Neurocomputing, 21 (1-3), 119-138. | Zbl 0917.68082

Cottrell M., Ibbou S., Letrémy P. (October-November 2004), SOM-based algorithms for qualitative variables, Neural Networks, 17 (8-9), 1149-1167. | Zbl 1078.68118

Cottrell M., Letrémy P. (January 2005), How to use the Kohonen algorithm to simultaneously analyze individuals and modalities in a survey, Neurocomputing, 63, 193-207.

De Reyniès A. (Septembre 2002), Classification de données symboliques : une extension de la méthode des nuées dynamiques, in : Actes du IXème congrès de la société Francophone de Classification, pp. 177-180.

De Reyniès A. ( 2003), Classification et discrimination en analyse de données symboliques. Thèse de doctorat, Université Paris Dauphine, Paris, France.

Diday E. ( 1971), La méthode des nuées dynamiques, Revue statistique appliquée, XIX (2), 19-34. | Numdam

Diday E., Govaert G. ( 1977), Classification automatique avec distances adaptatives, R.A.I.R.O. Informatique Computer Science, 11 (4), 329-349. | MR 495278 | Zbl 0375.62061

Diday E., Simon J. J. ( 1976), Clustering analysis, Fu, K. S. (Eds.), Digital Pattern Recognition. Springer, Heidelberg, 47-94. | Zbl 0331.62043

Dreyfus G., Martinez J.-M., Samuelides M., Gordon M. B., Badran F., Thiria S., Hérault L. ( 2002) Réseaux de neurones - méthodologie et applications; Eyrolles, Paris.

El Golli A. ( 2004), Extraction de données symboliques et cartes topologiques : Application aux données ayant une structure complexe. Thèse de doctorat, Université Paris-IX Dauphine, Paris, France.

El Golli A., Conan-Guez B., Rossi F. (November 2004), Self organizing map and symbolic data. Journal of Symbolic Data Analysis, 2(1).

Fort J.-C., Cottrell M., Letrémy P. ( 2001), Stochastic on-line algorithm versus batch algorithmfor quantization and self-organizing maps, in : Proceedings of Neural Networks for Signal Processing 2001, Falmouth, USA.

Foss A., Wang W., Zaïane O. R. (April 2001), A non-parametric approach to web log analysis, in : Proc. of Workshop on Web Mining in First International SIAM Conference on Data Mining (SDM2001), Chicago, IL, pp. 41-50.

Fu Y., Sandhu K., Shih M.-Y. ( 2000), A generalization-based approach to clustering of web usage sessions, in : Masand, Spiliopoulou (Eds.), Web Usage Analysis and User Profiling, Vol. 1836 of Lecture Notes in Artificial Intelligence, Springer, pp. 21-38.

Gaul W., Schmidt-Thieme L. ( 2000), Frequent generalized subsequences - a problem from web mining, in : Gaul, W., Opitz, O., Schader, M. (Eds.), Data Analysis, Scientific Modelling and Practical Application, Springer, Heidelberg, pp. 429-445.

Graepel T., Burger M., Obermayer K. (November 1998), Self-organizing maps : Generalizations and new optimization techniques, eurocomputing, 21, 173-190. | Zbl 0917.68182

Graepel T., Obermayer K. ( 1999), A stochastic self-organizing map for proximity data, Neural Computation, 11 (1), 139-155.

Hammer B., Micheli A., Sperduti A., Strickert M. (March 2004), A general framework for unsupervised processing of structured data, Neurocomputing, 57, 3-35.

Heskes T., Kappen B. ( 1993), Error potentials for self-organization, in : Proceedings of 1993 IEEE International Conference on Neural Networks (Joint FUZZ-IEEE'93 and ICNN'93 [IJCNN93]). Vol. III. IEEE/INNS, San Francisco, California, pp. 1219-1223.

Hotelling H. ( 1933), Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology, 24, 417-441, 498-520. | JFM 59.1182.04

Kohonen T. ( 1995, 1997 & 2001), Self-Organizing Maps, 3rd Edition, Vol. 30 of Springer Series in Information Sciences, Springer. | Zbl 0866.68085

Kohonen T. ( 1996), Self-organizing maps of symbol strings. Technical report a42, Laboratory of computer and information science, Helsinki University of technology, Finland. | MR 1324107

Kohonen T., Somervuo P. J. ( 1998), Self-organizing maps of symbol strings, Neurocomputing, 21, 19-30. | MR 1450869 | Zbl 0917.68177

Kohonen T., Somervuo P. J. ( 2002), How to make large self-organizing maps for nonvectorial data, Neural Networks, 15 (8), 945-952.

Levenshtein V.I. ( 1966), Binary codes capable of correcting deletions, insertions and reversais, Sov. Phys. Dokl, 6, 707-710. | MR 189928 | Zbl 0149.15905

Luotonen A. ( 1995), The common logfile format, http://www.w3.org/pub/WWW/Daemon/User/Config/Logging.html.

Macqueen J. ( 1965), Some methods for classification and analysis of multivariate observations, in : Proc. of the Fifth Berkeley Symposium on Math., Stat. and Prob.Vol. 1. pp. 281-297. | MR 214227 | Zbl 0214.46201

Matusita K. ( 1951), Decision rules based on distance for problems of fit, two samples and estimation, Ann. Math. Stat., 3, 1-30. | MR 73899 | Zbl 0065.12101

Matusita K. ( 1955), On the theory of statistical decision functions, Ann. Math. Stat., 26, 631-640. | MR 73899 | Zbl 0065.12101

Mobasher B.,Dai H., Luo T., Nakagawa M. (January 2002), Discovery and evaluation of aggregate usage profiles for web personalization, Data Mining and Knowledge Discovery, 6 (1), 61-82. | MR 1917931

Raggett D., Le Hors A., Jacobs I. (December 1999), HTML 4.01 specification, W3C recommendation, W3C, http://www.w3.org/TR/html4/.

Ramsay J., Silverman B. (June 1997, Functional Data Analysis, Springer Series in Statistics. Springer Verlag. | MR 2168993 | Zbl 0882.62002

Rossi F., Conan-Guez B., El Golli A. (April 2004), Clustering functional data with the som algorithm, in : Proceedings of ESANN 2004. Bruges, Belgium, pp. 305-312.

Somervuo P. J. ( 2004), Online algorithm for the self-organizing map of symbol strings, Neural Networks, 17 (1231-1239).

Srivastava J., Cooley R., Deshpande M., Tan P.-N. ( 2000), Web usage mining : Discovery and applications of usage patterns from web data, SIGKDD Explorations, 1 (2), 12-23.

Tanasa D., Trousse B. ( 2003), Le prétraitement des fichiers log web dans le web usage mining multi-sites, in : Journées Francophones de la toile.

Tanasa D., Trousse B. ( 2004a), Advanced data preprocessing for intersites web usage mining, IEEE Intelligent Systems, 19 (2), 59-65.

Tanasa D., Trousse B. ( 2004b), Data preprocessing for wum, IEEE Potentials, 23 (3), 22-25.

Tenenbaum J. B., De Silva V., Langford J. C. (December 2000), A global geometric framework for nonlinear dimensionality reduction, Science, 290 (5500), 2319-2323.

Thiria S., Lechevallier Y., Gascuel O., Canu S. ( 1997), Statistique et méthodes neuronales, Dunod, Paris.

Torgerson W. S. ( 1952), Multidimensional scaling : I. theory and method, Psychometrika, 17, 401-419. | MR 54219 | Zbl 0049.37603

Verde R., Decarvalho F., Lechevallier Y. ( 2000), A dynamical clustering algorithm for multi-nominal data, in :H.A.L. Kiers, J.-P. Rasson, P. G., Schader, M. (Eds.), Data Analysis, Classification, and Related Method, Springer-Verlag, Heidelberg, pp. 387-394. | MR 1848204 | Zbl 1026.62069

W3C HTML WORKING GROUP (August 2002), XHTML 1.0 the Extensible Hyper Text Markup Language, W3C recommendation, W3C, second Edition, http://www.w3.org/TR/xhtml1/.

Wang J.-L., Wang X., Lin K.-I., Shasha D., Shapiro B. A., Zhang K. ( 1999), Evaluating a class of distance-mapping algorithms for data mining and clustering, in : Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 307-311.