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Obtaining common pruned trees

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Abstract

Given two or more dendrograms (rooted tree diagrams) based on the same set of objects, ways are presented of defining and obtaining common pruned trees. Bounds on the size of a largest common pruned tree are introduced, as is a categorization of objects according to whether they belong to all, some, or no largest common pruned trees. Also described is a procedure for regrafting pruned branches, yielding trees for which one can assess the reliability of the depicted relationships. The tree obtained by regrafting branches on to a largest common pruned tree is shown to contain all the classes present in the strict consensus tree. The theory is illustrated by application to two classifications of a set of forty-nine stratigraphical pollen spectra.

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References

  • ADAMS, E.N. (1972), “Consensus Techniques and the Comparison of Taxonomic Trees,”Systematic Zoology, 21, 390–397.

    Google Scholar 

  • ANSI (1978), “American National Standard Programming Language FORTRAN, ANSI X3.9- 1978,” American National Standards Institute, New York.

    Google Scholar 

  • BIRKS, H.H., and MATHEWES, R.W. (1978), “Studies in the Vegetational History of Scotland V,”New Phytologist, 80, 455–484.

    Google Scholar 

  • COOK, R.D., and WEISBERG, S. (1982),Residuals and Influence in Regression, London: Chapman and Hall.

    Google Scholar 

  • DAY, W.H.E., and EDELSBRUNNER, H. (1984), “Efficient Algorithms for Agglomerative Hierarchical Clustering Methods,”Journal of Classification, 1, 7–24.

    Google Scholar 

  • DEC (1982), “VAX-11 FORTRAN Language Reference Manual,” Digital Equipment Corporation, Maynard, Massachusetts.

    Google Scholar 

  • DIDAY, E. (1982), “Croisements, Ordres et Ultramétriques: Application à la Recherche de Consensus en Classification Automatique”, Rapport 144, INRIA, Centre de Rocquencourt, Le Chesnay.

    Google Scholar 

  • FINDEN, C.R. (1983), “CMTREE Manual,” Report, Department of Statistics, University of St. Andrews.

  • FINDEN, C.R. (1984a), “FSTREE Manual,” Report, Department of Statistics, University of St. Andrews.

  • FINDEN, C.R. (1984b), “UBTREE Manual,” Report, Department of Statistics, University of St. Andrews.

  • FINDEN, C.R. (1984c), “LMTREE Manual,” Report, Department of Statistics, University of St. Andrews.

  • GAREY, M.R., and JOHNSON, D.S. (1979),Computers and Intractability: A Guide to the Theory of NP-Completeness, New York: W.H. Freeman and Co.

    Google Scholar 

  • GORDON, A.D. (1979), “A Measure of the Agreement between Rankings,”Biometrika, 66, 7–15.

    Google Scholar 

  • GORDON, A.D. (1980), “On the Assessment and Comparison of Classifications,” inAnalyse de Données et Informatique, ed. R. Tomassone, Le Chesnay: INRIA.

    Google Scholar 

  • GORDON, A.D. (1981),Classification: Methods for the Exploratory Analysis of Multivariate Data, London: Chapman and Hall.

    Google Scholar 

  • HARTIGAN, J.A. (1975),Clustering Algorithms, New York: John Wiley.

    Google Scholar 

  • KRUSKAL, J. (1977), “The Relationship between Multidimensional Scaling and Clustering,” inClassification and Clustering, ed. J. Van Ryzin, New York: Academic Press.

    Google Scholar 

  • MAIER, D. (1978), “The Complexity of Some Problems on Subsequences and Supersequences,”Journal of the Association for Computing Machinery, 25, 322–336.

    Google Scholar 

  • MARGUSH, T., and MC MORRIS, F.R. (1981), “Consensus n-Trees,”Bulletin of Mathematical Biology, 43, 239–244.

    Google Scholar 

  • MURTAGH, F. (1984), “Counting Dendograms: A Survey,”Discrete Applied Mathematics, 7, 191–199.

    Google Scholar 

  • NEUMANN, D.A. (1983), “Faithful Consensus Methods for n-Trees,”Mathematical Biosciences, 63, 271–287.

    Google Scholar 

  • ROHLF, F.J. (1970), “Adaptive Hierarchical Clustering Schemes,”Systematic Zoology, 19, 58–82.

    Google Scholar 

  • SIBSON, R. (1972), “Order Invariant Methods for Data Analysis,”Journal of the Royal Statistical Society, Series B, 34, 311–349.

    Google Scholar 

  • SOKAL, R.R., and ROHLF, F.J. (1981), “Taxonomic Congruence in the Leptopodomorpha Re-examined,”Systematic Zoology, 30, 309–325.

    Google Scholar 

  • WAGNER, R.A., and FISCHER, M.J. (1974), “The String-to-String Correction Problem,”Journal of the Association for Computing Machinery, 21, 168–173.

    Google Scholar 

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This work was supported by the Science and Engineering Research Council. The authors are grateful to the referees for constructive criticisms of an earlier version of the paper, and to Dr. J.T. Henderson for advice on PASCAL.

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Finden, C.R., Gordon, A.D. Obtaining common pruned trees. Journal of Classification 2, 255–276 (1985). https://doi.org/10.1007/BF01908078

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