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- W2145128519 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 Guenoche 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." @default.
- W2145128519 created "2016-06-24" @default.
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- W2145128519 date "2002-01-01" @default.
- W2145128519 modified "2023-10-02" @default.
- W2145128519 title "Comparison of Four Methods for Inferring Additive Trees from Incomplete Dissimilarity Matrices" @default.
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- W2145128519 doi "https://doi.org/10.1007/978-3-642-56181-8_41" @default.
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