@article{RSA_2003__51_4_5_0, author = {Chavent, M. and De Carvalho, F. de A. T. and Lechevallier, Y. and Verde, R.}, title = {Trois nouvelles m\'ethodes de classification automatique de donn\'ees symboliques de type intervalle}, journal = {Revue de Statistique Appliqu\'ee}, pages = {5--29}, publisher = {Soci\'et\'e fran\c{c}aise de statistique}, volume = {51}, number = {4}, year = {2003}, language = {fr}, url = {http://www.numdam.org/item/RSA_2003__51_4_5_0/} }
TY - JOUR AU - Chavent, M. AU - De Carvalho, F. de A. T. AU - Lechevallier, Y. AU - Verde, R. TI - Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle JO - Revue de Statistique Appliquée PY - 2003 SP - 5 EP - 29 VL - 51 IS - 4 PB - Société française de statistique UR - http://www.numdam.org/item/RSA_2003__51_4_5_0/ LA - fr ID - RSA_2003__51_4_5_0 ER -
%0 Journal Article %A Chavent, M. %A De Carvalho, F. de A. T. %A Lechevallier, Y. %A Verde, R. %T Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle %J Revue de Statistique Appliquée %D 2003 %P 5-29 %V 51 %N 4 %I Société française de statistique %U http://www.numdam.org/item/RSA_2003__51_4_5_0/ %G fr %F RSA_2003__51_4_5_0
Chavent, M.; De Carvalho, F. de A. T.; Lechevallier, Y.; Verde, R. Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle. Revue de Statistique Appliquée, Tome 51 (2003) no. 4, pp. 5-29. http://www.numdam.org/item/RSA_2003__51_4_5_0/
[AUB94] Initiation à l'analyse appliquée, Masson. | MR | Zbl
(1994),[BRE 84] Classification and regression trees, Chapman Hall. | MR
, , , (1984),[BOC 00] BOCK H. H., DIDAY E. (eds.) (2000), Analysis of Symbolic Data, Exploratory methods for extracting statistical information from complex data. Studies in Classification, Data Analysis and Knowledge Organisation, Springer-Verlag. | MR | Zbl
[CEL 89] Classification Automatique des Données. Bordas, Paris.
, , , (1989),[CHA 97] Analyse des Données Symboliques. Une méthode divisive de classification. Thèse de l'Université de PARIS-IX Dauphine.
(1997),[DCA 94] Proximity coefficients between Boolean symbolic objects, in New Approaches in Classification and Data Analysis, Diday et al. (Eds.), Springer Verlag, Heidelberg, 387-394. | MR
(1994),[DCA 98] Extension based proximities between Boolean symbolic objects, in Data Science, Classification and Related Methods, Hayashi, C. et al. (eds.), Springer-Verlag, Tokyo, 370-378. | Zbl
(1998),[DCS 98] Statistical proximity functions of Boolean symbolic objects based on histograms. In : Rizzi, A., Vichi, M., Bock, H.-H. (Eds.) : Advances in Data Science and Classification, Springer-Verlag, Heidelberg, 391- 396 | MR | Zbl
, (1998),[DCA 00] Symbolic approach to classify large data sets, in : Data Analysis, Classification, and Related Methods, Kiers, H.A.L. et al. (Eds.), Springer, 375-380. | Zbl
, , (2000),[DVL 99] A dynamical clustering of symbolic objects based on a context dependent proximity measure. In : Bacelar-Nicolau, H., Nicolau, F.C. and Janssen, J. (Eds.) : Proc. IX International Symposium - ASMDA'99. LEAD, Univ. de Lisboa, 237-242.
, et (1999),[DID 71] Le méthode des Nuées dynamiques, in Revue de Statistique Appliquée, 19, 2, 19-34. | Numdam
(1971),[DID 88] The symbolic approach in clustering and related methods of data analysis : The basic choice. In Proc. IFCS-97, Bock, H.-H. (Eds), Springer-Verlag, Heidelberg, 673-684.
(1988),[DID 98] Symbolic Data Analysis : a Mathematical Framework and Tool for Data Mining, in New Andvances in Data Science and Classification, Rizzi, A. et al. (eds.), Springer -Verlag, Heidelberg, 409-416. | Zbl
(1998),[DIS 76] Clustering Analysis. In : Fu, K. S. (Eds.) : Digital Pattern Recognition. Springer-Verlag, Heidelberg, 47-94. | Zbl
AND (1976),[DID 80] Clustering in pattern recognition, NATO Advanced study Institute on Digital Image Processing and Analysis, Bonas. Available at INRIA-Rocquencourt. | Zbl
, , et (1980),[ICY 94] Generalized Minkowsky Metrics for Mixed Feature Type Data Analysis. IEEE Transactions System, Man and Cybernetics 24, 698-708. | MR
, (1994),[IYD 96] A fuzzy symbolic pattern classifier, in : Ordinal and Symbolic Data Analysis, Diday, E. et al. (Eds.), Springer, 92-102. | Zbl
, , (1996),[LEC 97] Classification non supervisée, in Statistique et méthodes neuronales, Thiria, Lechevallier et al. (Eds.), Dunod, Chap. 10,171- 189.
(1997),[LER 79] La méthode des pôles d'attraction - La méthode des pôles d'agrégation. Thèse de Diplôme de docteur de 3e cycle. Université Paris VI, 106-116.
(1979),[MIC 80] Knowledge acquisition through conceptual clustering : A theoretical framework and an algorithm for partitioning data into conjunctive concepts. A special Issue on Knowledge Acquisition and Induction. Policy Analysis and Information Systems, 3. | MR
(1980),[MDS 81] A recent advance in data analysis : Clustering Objects into classes characterized by conjunctive concepts. In : Kanal L. N. and Rosenfeld A. (Eds.) : Progress in pattern recognition. North-Holland, 33-56.
, , (1981),[VDL 00] A Dynamical Clustering Algorithm for Multi-Nominal Data. In : H.A.L. Kiers, J.-P. Rasson, P.J.F. Groenen and M. Schader (Eds.) : Data Analysis, Classification, and Related Methods, Springer-Verlag, Heidelberg, 387-394. | MR | Zbl
, , (2000),[VDL 01] A dynamical clustering algorithm for symbolic data. Tutorial Symbolic Data Analysis, GfK1 Conference, Munich.
, DE , (2001),