Abstract
The least squares algorithm for fitting ultrametric trees to proximity data originally proposed by Carroll and Pruzansky and further elaborated by De Soete is extended to handle missing data. A Monte Carlo evaluation reveals that the algorithm is capable of recovering an ultrametric tree underlying an incomplete set of error-perturbed dissimilarities quite well.
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Geert De Soete is “Aangesteld Navorser” of the Belgian “National Fonds voor Wetenschappelijk Onderzoek.”
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De Soete, G. Ultrametric tree representations of incomplete dissimilarity data. Journal of Classification 1, 235–242 (1984). https://doi.org/10.1007/BF01890124
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DOI: https://doi.org/10.1007/BF01890124