Abstract
Critchley and Van Cutsem [7] recently developed the properties of dissimilarities and ultrametrics with values in an ordered set. This theoretical definition allows consideration of a pair of ultrametrics as a two dimensional ultrametric with values in ℝ+ × ℝ+, and provides a good framework to study the dependence between two ultrametrics. In this paper, we just present the basic definitions for introducing the definition of some new indices of dependence or of comparison of two real-valued ultrametrics. Details can be found in Benkaraache’s thesis [4].
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References
ADAMS II, E.N. (1972): Consensus techniques and the comparison of taxonomic trees. Syst.Zool. 21, 390–397.
BARTHÉLÉMY, J.P., LECLERC, B., and MONJARDET, B. (1984): Quelques aspects du consensus en classification. In Diday, E., et al. (Ed.) Data analysis and informatics 3. North-Holland.
BARTHÉLÉMY, J.P., LECLERC, B., and MONJARDET, B. (1984): Ensembles ordonnés et taxonomie mathématique. Annals of Discrete Mathematics. 23, 523–548.
BENKARAACHE, T., (1993): Problèmes de validité en classification et quelques gènèralisations aux ultramétriques à valeurs dans un ensemble ordonné. Thèse de Doctorat de 1’Université Joseph Fourier. Grenoble. Septembre 1993.
BENZÉCRI, J.P. (1973): L’Analyse des Données. I. La Taxinomie. Dunod, Paris.
CRITCHLEY, F. (1983): Ziggurats and dendrograms. Research Report of Department of Statistics of University of Warwick, Coventry Sept. 83. 10 pages.
CRITCHLEY, F., and VAN CUTSEM, B. (1992): An order-theoretic unification and generalisation of certain fundamental bijections in mathematical classification. Part I and II. Common Research Report of both Department of Statistics, University of Warwick, Coventry and Laboratoire Modélisation et Calcul — IMAG, Universitě Joseph Fourier, Grenoble. To appear in B. Van Cutsem (ed.): Classification and Dissimilarity Analysis. Lecture Notes in Statistics. Springer Verlag.
CRITCHLEY, F., and VAN CUTSEM, B. (1993): Some new useful Representations of Dissimilarities in Mathematical Classification. In O. Opitz et al. Information and Classification. Proc. 16th Annual Conference of the Gesellschaft für Klassifikation, Dortmund, April 1–3, 1992. Springer Verlag. Studies in classification, data analysis, and knowledge organization.
DAY, W.H.E. (1986): Foreword: Comparison and Consensus of Classifications. J. of Classification. 3, 183–185.
FAITH, D.P. (1984): Pattern of sensibility of association measures in numerical taxonomy. Math. Biosc. 69, 199–207.
FAITH, D.P., and BELBIN, L. (1986): Comparisons of classifications using measures intermediate between metric dissimilarity and consensus similarity. J. of Classification. 3, 257–280.
FOWLKES, E.B., and MALLOWS, C.L. (1983): A method for comparing two hierarchical clusterings. J.A.S.A.. 78, 553–569. See also Rejoinder, Ibid. p. 584
GOODMAN, L.A.J., and KRUSKAL, W.H. (1954): Measures of association for cross classification. Br. J. Math. Stat. Psychol.. 29, 190–241.
HUBERT, L.J., and SCHULTZ, J. (1976): The comparison of fitting of given classifications schemes. Br. J.A.S.A.. 49, 732–764.
HUBERT, L.J., and BAKER, F.B. (1976): Quadratic assignment as a general data analysis strategy. J. of Math. Psychol. 16, 233–253.
JAMBU, M. (1975): Quelques critères de comparaison des hiérarchies indicées produites en classification automatique. Consommation. 1, 56–84.
JANOWITZ, M.F. (1978): An order theoretic model for cluster analysis. SIAM J. Appli. Math. 34, 55–72.
JAIN, A.K., and DUBES, R.C. (1988): Algorithms for clustering data. Prentice Hall, Inc.
LAPOINTE, F.J., and LEGENDRE P. (1990): A statistical framework to test the consensus of two nested classifications. Syst. Zool. 39(1), 1–13.
LECLERC, B. (1985): La comparaison des hiérarchies: indices et métriques. Math. Sc. Hum.. 92, 5–40.
LERMAN, I.C. (1981): Classification et analyse ordinale des données. Dunod.
MANTEL, N. (1967): The detection of desease clustering and a generalised version regression approach. Cancer. Research. 27, 209–220.
MICKEVICH, M.F. (1978): Taxonomic congruence. Syst. Zool.. 27, 143–158.
MURTAGH, F. (1983): A probability theory of hierarchic clustering using random dendrograms. J. of Statistics and Computer Simulation. 18, 145–157.
SCHUH, R.T., and FARRIS, J.S.(1981): Analysis of taxonomie congruence among morphological, ecological and biogeographic data sets for the Leptodomorpha (Hemiptera). Syst. Zool.. 30, 331–351.
SNEATH P.H.A., and SOKAL, R.R. (1973): Numerical Taxonomy. W.H. Freedman and Co.
SOKAL, R.R., and ROHLF, F.J. (1962): The comparison of dendrograms by objective methods. Taxon. 11, 33–40.
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Benkaraache, T., Van Cutsem, B. (1994). Comparison of hierarchical classifications. In: Diday, E., Lechevallier, Y., Schader, M., Bertrand, P., Burtschy, B. (eds) New Approaches in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-51175-2_8
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