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Traditionally, there are three species of classification: unsupervised, supervised, and semi-supervised. Supervised and semi-supervised classification differ by whether or not weight is given to unlabelled observations in the classification procedure. In unsupervised classification, or...
A model is presented for analyzing general multivariate data. The model puts as its prime objective the dimensionality reduction of the multivariate problem. The only requirement of the model is that the input data to the statistical analysis be a covariance matrix, a correlation matrix, or more...
The paper presents a least squares framework for divisive clustering. Two popular divisive clustering methods, Bisecting K-Means and Principal Direction Division, appear to be versions of the same least squares approach. The PDD recently has been enhanced with a stopping criterion taking into...
One of the most important distinctions that must be made in clustering research is the difference between models (or problems) and the methods for solving those problems. Nowhere is this more evident than with the evaluation of the popular affinity propagation algorithm (apcluster.m), which is a...
Two-mode partitioning applications are increasingly common in the physical and social sciences with a variety of models and methods spanning these applications. Two-mode KL-means partitioning (TMKLMP) is one type of two-mode partitioning model with a conceptual appeal that stems largely from the...
Building on probabilistic models for interval-valued variables, parametric classification rules, based on Normal or Skew-Normal distributions, are derived for interval data. The performance of such rules is then compared with distancebased methods previously investigated. The results show that...
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