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Whenevern objects are characterized by a matrix of pairwise dissimilarities, they may be clustered by any of a number of sequential, agglomerative, hierarchical, nonoverlapping (SAHN) clustering methods. These SAHN clustering methods are defined by a paradigmatic algorithm that usually requires...
Models for the representation of proximity data (similarities/dissimilarities) can be categorized into one of three groups of models: continuous spatial models, discrete nonspatial models, and hybrid models (which combine aspects of both spatial and discrete models). Multidimensional scaling...
A method is presented for the graphic display of proximity matrices as a complement to the common data analysis techniques of hierarchical clustering. The procedure involves the use of computer generated shaded matrices based on unclassed choropleth mapping in conjunction with a strategy for...
The nearest neighbor interchange (nni) metric is a distance measure providing a quantitative measure of dissimilarity between two unrooted binary trees with labeled leaves. The metric has a transparent definition in terms of a simple transformation of binary trees, but its use in nontrivial...
Analytic procedures for classifying objects are commonly based on the product-moment correlation as a measure of object similarity. This statistic, however, generally does not represent an invariant index of similarity between two objects if they are measured along different bipolar variables...
A general set of multidimensional unfolding models and algorithms is presented to analyze preference or dominance data. This class of models termed GENFOLD2 (GENeral UnFOLDing Analysis-Version 2) allows one to perform internal or external analysis, constrained or unconstrained analysis,...
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