Une nouvelle approche des problèmes de classification automatique

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1. [1] P.A.W. LEWIS, "Stochastic Point Processes", Wiley, New York, 1972. Zbl0238.00014MR359226
2. [2] K. KRICKEBERG, "Moments of point processes", Lecture Notes in Mathematics 296, pp. 70-101, 1973. Zbl0268.60053MR378095
3. [3] J.P. RASSON, "Estimation de formes convexes du plan", Statistique et Analyse des Données 1, pp. 31-46, 1979. 
4. [4] J.A. HARTIGAN, "Clustering Algorithms", Wiley, New York, 1975. Zbl0372.62040MR405726
5. [5] H. SPATH, "Cluster Analysis Algorithms", Wiley, Chichester, 1980. 
6. [6] R.L. GRAHAM, "An efficient algorithm for determining the convex hull of a finite planar set", Information Processing Letters 1, pp. 132-133, 1972. Zbl0236.68013
7. [7] F.P. PREPARATA & S.J. HONG, "Convex hulls of finite sets in two and three dimensions", Communications of the ACM 20, 2, pp. 87-93, 1977. Zbl0342.68030MR488985
8. [8] A. C. YAO, "A lower bound to finding convex hulls", Stanford University Computer Science Department Report STAN-CS-79-733, 1979. Zbl0468.68080
9. [9] J.L. BENTLEY & M.I. SHAMOS, "Divide and conquer for linear expected time", Information Processing Letters 7, 2, pp. 87-91, 1978. Zbl0404.68046MR483687
10. [10] L. DEVROYE, "A note on finding convex hulls via maximal vectors", Information Processing Letters 11, 1, pp. 53-56, 1980. Zbl0444.68063MR585406
11. [11] J.L. BENTLEY, M.G. FAUST & F.P. PREPARATA, "Approximation Algorithms for convex hulls", Communications of the ACM 25, 1, pp. 64-68, 1982. Zbl0466.68059MR662578
12. [12] J. COHEN & T. HICKEY, "Two algorithms for determining volumes of convex polyhedra", Journal of the ACM 26, 3, pp. 401-414, 1979. Zbl0403.68067MR535261
13. [13] L. FISHER & J.W. VAN NESS, "Admissible clustering procedures", Biometrika 58, pp. 91-104, 1971. Zbl0224.62030MR347007
14. [14] SIDAK, "Some ideas for the comparison of clustering procedures", 12th European Meeting of Statisticians, Varna, 1979. 
15. [15] G. NAGY, "State of the art in pattern recognition", Proceedings of the IEEE 56, 5, pp. 836-862, 1968. 
16. [16] E. RUSPINI, "A new approach to clustering", Information and Control 15, pp. 22-32, 1969. Zbl0192.57101
17. [17] E. DIDAY, "Nouvelles méthodes et nouveaux concepts en classification automatique et reconnaissance des formes", Thèse d'Etat, Paris, 1972. Zbl0251.68051
18. [18] P. HANSEN & M. DELATTRE, "Complete-link cluster analysis by graph coloring" J. Am. Statist. Assoc. 73, pp. 397-403, 1978. Zbl0432.05004
19. [19] D.R. CHAND & S.S. KAPUR, "An algorithm for convex polytopes", Journal of the ACM 17, pp. 78-86, 1970. Zbl0199.50902MR278177
20. [20] T. LENGYEL, "A note on the number of clustering", 12th European Meeting of Statisticians, Varna, 1979. Zbl0466.62046
21. [21] J.P. BENZECRI et coll., "L'Analyse des Données. Vol. I : La Taxinomie", Dunod, Paris, 1973. Zbl0503.62002MR593138
22. [22] E. DIDAY et coll., "Optimisation en classification automatique", INRIA, 1979. Zbl0471.62056
23. [23] S. REGNIER, "Sur quelques aspects mathématiques des problèmes de classification automatique", ICC Bulletin 4, pp. 175-191, 1965. Zbl0548.62040
24. [24] I.C. LERMAN, "Les Bases de la Classification Automatique", Gauthier-Villars, Paris, 1970. Zbl0199.51402MR349079
25. [25] SAID ALI HASSAN, "Recherche des sommets et arêtes de l'enveloppe convexe d'un nombre fini de points", Thèse de Docteur-Ingénieur, Toulouse, 1981. 

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