Journal ArticleDOI
Additive similarity trees
Shmuel Sattath,Amos Tversky +1 more
TLDR
A computer program, ADDTREE, for the construction of additive trees is described and applied to several sets of data, and some empirical and theoretical advantages of tree representations over spatial representations of proximity data are illustrated.Abstract:
Similarity data can be represented by additive trees. In this model, objects are represented by the external nodes of a tree, and the dissimilarity between objects is the length of the path joining them. The additive tree is less restrictive than the ultrametric tree, commonly known as the hierarchical clustering scheme. The two representations are characterized and compared. A computer program, ADDTREE, for the construction of additive trees is described and applied to several sets of data. A comparison of these results to the results of multidimensional scaling illustrates some empirical and theoretical advantages of tree representations over spatial representations of proximity data.read more
Citations
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Journal ArticleDOI
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.
Journal ArticleDOI
Features of Similarity
TL;DR: The metric and dimensional assumptions that underlie the geometric representation of similarity are questioned on both theoretical and empirical grounds and a set of qualitative assumptions are shown to imply the contrast model, which expresses the similarity between objects as a linear combination of the measures of their common and distinctive features.
Journal ArticleDOI
Quartet Puzzling: A Quartet Maximum-Likelihood Method for Reconstructing Tree Topologies
TL;DR: A versatile method, quartet puzzling, is introduced to reconstruct the topology (branching pattern) of a phylogenetic tree based on DNA or amino acid sequence data and outperforms neighbor joining in some cases with high transition/transversion bias.
Journal ArticleDOI
Multidimensional scaling: Multidimensional scaling
TL;DR: Key aspects of performing MDS are discussed, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output.
Book
Cluster analysis
TL;DR: Cluster analysis is a multivariate procedure for detecting natural groupings in data that resembles discriminant analysis in one respect—the researcher seeks to classify a set of objects into subgroups although neither the number nor members of the subgroups are known.
References
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Journal ArticleDOI
Features of Similarity
TL;DR: The metric and dimensional assumptions that underlie the geometric representation of similarity are questioned on both theoretical and empirical grounds and a set of qualitative assumptions are shown to imply the contrast model, which expresses the similarity between objects as a linear combination of the measures of their common and distinctive features.
Journal ArticleDOI
Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
TL;DR: The fundamental hypothesis is that dissimilarities and distances are monotonically related, and a quantitative, intuitively satisfying measure of goodness of fit is defined to this hypothesis.
Book
Numerical Taxonomy: The Principles and Practice of Numerical Classification
P.H.A. Sneath,Robert R. Sokal +1 more
Journal ArticleDOI
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.
Journal ArticleDOI
A general nonmetric technique for finding the smallest coordinate space for a configuration of points
TL;DR: In this article, a general coefficient of monotonicity, whose maximization is equivalent to optimal satisfaction of the Monotonicity condition, is defined, and which allows various options both for treatment of ties and for weighting error-of-fit.