Abstract
Agglomeration and addition are the two main algorithmic schemes for constructing a tree distance from a dissimilarity matrix. The former scheme iteratively agglomerates pairs of leaves to form larger and larger clusters, while the latter proceeds by stepwise addition of objects to a growing tree. A third approach involves improving the global fitness of an initial tree by exchanging subtrees. This article suggests that the shape of inferred trees partly depends on the chosen algorithmic scheme: agglomeration tends to produce compact and bushy tree shapes, while addition and exchange have a preference for sparse and chain-like trees. This phenomenon is explained by the difference between the a priori probability distributions induced by each scheme. An illustration is provided with the Mitochondrial Eve data set (Vigilant et al. 1991), and the practical impacts are discussed.
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Gascuel, O. (2000). Evidence for a Relationship Between Algorithmic Scheme and Shape of Inferred Trees. In: Gaul, W., Opitz, O., Schader, M. (eds) Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58250-9_13
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DOI: https://doi.org/10.1007/978-3-642-58250-9_13
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