The Geometry of the Neighbor-Joining Algorithm for Small Trees
AB '08 Proceedings of the 3rd international conference on Algebraic Biology
Accelerating the neighbor-joining algorithm using the adaptive bucket data structure
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Large-scale neighbor-joining with NINJA
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
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The Neighbor-Joining (NJ) method of Saitou and Nei is the most widely useddistance based method in phylogenetic analysis. Central to the method is the selectioncriterion, the formula used to choose which pair of objects to amalgamate next. Herewe analyze the NJ selection criterion using an axiomatic approach. We show that anyselection criterion that is linear, permutation equivariant, statistically consistent and basedsolely on distance data will give the same trees as those created by NJ.