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Principles of numerical taxonomy
Robert R. Sokal,P.H.A. Sneath +1 more
TLDR
The authors continued the story of psychology with added research and enhanced content from the most dynamic areas of the field, such as cognition, gender and diversity studies, neuroscience and more, while at the same time using the most effective teaching approaches and learning tools.Abstract:
This new edition continues the story of psychology with added research and enhanced content from the most dynamic areas of the field--cognition, gender and diversity studies, neuroscience and more, while at the same time using the most effective teaching approaches and learning toolsread more
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The neighbor-joining method: a new method for reconstructing phylogenetic trees.
Naruya Saitou,Masatoshi Nei +1 more
TL;DR: The neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods for reconstructing phylogenetic trees from evolutionary distance data.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
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Genetic distance between populations
TL;DR: If enough data are available, genetic distance between any pair of organisms can be measured in terms of D, and this measure is applicable to any kind of organism without regard to ploidy or mating scheme.
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Data clustering: 50 years beyond K-means
TL;DR: A brief overview of clustering is provided, well known clustering methods are summarized, the major challenges and key issues in designing clustering algorithms are discussed, and some of the emerging and useful research directions are pointed out.
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Hierarchical clustering schemes
TL;DR: A useful correspondence is developed between any hierarchical system of such clusters, and a particular type of distance measure, that gives rise to two methods of clustering that are computationally rapid and invariant under monotonic transformations of the data.