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Comparison of Four Methods for Inferring Additive Trees from Incomplete Dissimilarity Matrices

  • Vladimir Makarenkov
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The problem of inference of an additive tree from an incomplete dissimilarity matrix is known to be very delicate. As a solution to this problem, it has been suggested either to estimate the missing entries of a given partial dissimilarity matrix prior to tree reconstruction (De Soete, 1984 and Landry et al., 1997) or directly reconstruct an additive tree from incomplete data (Makarenkov and Leclerc, 1999 and Guénoche and Leclerc, 2001). In this paper, I propose a new method, that is based on the least-squares approximation, for inferring additive trees from partial dissimilarity matrices. The capacity of the new method to recover a true tree structure will be compared to those of three well-known techniques for tree reconstruction from partial data. The new method will be proven to work better than widely used Ultrametric and Additive reconstruction techniques, as well as the recently proposed Triangle method on incomplete dissimilarity matrices of different sizes and under different noise conditions.

Keywords

Additive Tree Dissimilarity Matrix Tree Reconstruction Partial Matrice Dissimilarity Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Vladimir Makarenkov
    • 1
    • 2
  1. 1.Département d’informatiqueUniversité du Québec à MontréalMontréalCanada
  2. 2.Institute of Control SciencesMoscowRussia

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