NP-hard problems in hierarchical-tree clustering
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l∞ -approximation via subdominants
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Fitting tree metrics: Hierarchical clustering and Phylogeny
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Fitting Tree Metrics: Hierarchical Clustering and Phylogeny
SIAM Journal on Computing
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In phylogenetic analysis, one searches for phylogenetic trees that reflect observed similarity between a collection of species in question. To this end, one often invokes two simple facts: (i) Any tree is completely determined by the metric it induces on its leaves (which represent the species). (ii) The resulting metrics are characterized by their property of being additive or, in the case of dated rooted trees, ultra-additive. Consequently, searching for additive or ultra-additive metrics A that best approximate the metric D encoding the observed similarities is a standard task in phylogenetic analysis. Remarkably, while there are efficient algorithms for constructing optimal ultra-additive approximations, the problem of finding optimal additive approximations in the l"1 or l"~ sense is NP-hard. In the context of the theory of @d-hyperbolic groups, however, good additive approximations A of a metric D were found by Gromov already in 1988 and shown to satisfy the bound@?D-A@?"~=