Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle

M. Chavent; F. de A. T. De Carvalho; Y. Lechevallier; R. Verde

Revue de Statistique Appliquée (2003)

  • Volume: 51, Issue: 4, page 5-29
  • ISSN: 0035-175X

How to cite


Chavent, M., et al. "Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle." Revue de Statistique Appliquée 51.4 (2003): 5-29. <>.

author = {Chavent, M., De Carvalho, F. de A. T., Lechevallier, Y., Verde, R.},
journal = {Revue de Statistique Appliquée},
language = {fre},
number = {4},
pages = {5-29},
publisher = {Société française de statistique},
title = {Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle},
url = {},
volume = {51},
year = {2003},

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
PB - Société française de statistique
VL - 51
IS - 4
SP - 5
EP - 29
LA - fre
UR -
ER -


  1. [AUB94] Aubin J.-P. (1994), Initiation à l'analyse appliquée, Masson. Zbl0809.90002MR1271774
  2. [BRE 84] Breiman L., Friedman J., Olshen R., Stone C. (1984), Classification and regression trees, Chapman Hall. Zbl0541.62042MR726392
  3. [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. Zbl0978.62003MR1792132
  4. [CEL 89] Celeux G., Diday E., Govaert, G. Lechevallier Y., Ralam-Bondrainy H. (1989), Classification Automatique des Données. Bordas, Paris. 
  5. [CHA 97] Chavent M. (1997), Analyse des Données Symboliques. Une méthode divisive de classification. Thèse de l'Université de PARIS-IXDauphine. 
  6. [DCA 94] De Carvalho F.A.T. (1994), Proximity coefficients between Boolean symbolic objects, in New Approaches in Classification and Data Analysis, Diday et al. (Eds.), Springer Verlag, Heidelberg, 387-394. MR1415847
  7. [DCA 98] De Carvalho F.A.T. (1998), Extension based proximities between Boolean symbolic objects, in Data Science, Classification and Related Methods, Hayashi, C. et al. (eds.), Springer-Verlag, Tokyo, 370-378. Zbl0894.62006
  8. [DCS 98] De Carvalho F.A.T., Souza R.M.C. (1998), 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 Zbl1052.62533MR1675089
  9. [DCA 00] De Carvalho F.A.T., Anselmo C.A.F., Souza, R.M.C.R. (2000), Symbolic approach to classify large data sets, in : Data Analysis, Classification, and Related Methods, Kiers, H.A.L. et al. (Eds.), Springer, 375-380. Zbl1102.62308
  10. [DVL 99] De Carvalho F.A.T., Verde, R. et Lechevallier Y. (1999), 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. 
  11. [DID 71] Diday E. (1971), Le méthode des Nuées dynamiques, in Revue de Statistique Appliquée, 19, 2, 19-34. 
  12. [DID 88] Diday E. (1988), 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. 
  13. [DID 98] Diday E. (1998), 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. Zbl1052.68615
  14. [DIS 76] Dida Y.E. AND Simon J.C. (1976), Clustering Analysis. In : Fu, K. S. (Eds.) : Digital Pattern Recognition. Springer-Verlag, Heidelberg, 47-94. Zbl0331.62043
  15. [DID 80] Diday E., Govaert G., Lechevallier Y. et Sidi J. (1980), Clustering in pattern recognition, NATO Advanced study Institute on Digital Image Processing and Analysis, Bonas. Available at INRIA-Rocquencourt. Zbl0503.68065
  16. [ICY 94] Ichino, M., Yaguchi H. (1994), Generalized Minkowsky Metrics for Mixed Feature Type Data Analysis. IEEE Transactions System, Man and Cybernetics24, 698-708. MR1280804
  17. [IYD 96] Ichino M., Yaguchi H., Diday E. (1996), A fuzzy symbolic pattern classifier, in : Ordinal and Symbolic Data Analysis, Diday, E. et al. (Eds.), Springer, 92-102. Zbl0896.68124
  18. [LEC 97] Lechevallier Y. (1997), Classification non supervisée, in Statistique et méthodes neuronales, Thiria, Lechevallier et al. (Eds.), Dunod, Chap. 10,171- 189. 
  19. [LER 79] Leredde H. (1979), 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. 
  20. [MIC 80] Michalski R.S. (1980), 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. MR599671
  21. [MDS 81] Michalski R.S., Diday E., Stepp R.E. (1981), 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. 
  22. [VDL 00] Verde R., De Carvalho F.A.T., Lechevallier Y. (2000), 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. Zbl1026.62069MR1848204
  23. [VDL 01] Verde R., DE Carvalho F.A.T., Lechevallier Y. (2001), A dynamical clustering algorithm for symbolic data. Tutorial Symbolic Data Analysis, GfK1 Conference, Munich. Zbl1026.62069

NotesEmbed ?


You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.


Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.