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Information measures

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Abstract This article presents an overview of the concept of information about random outcomes and measures that quantify information provided by probability distributions. We also provide a few examples and illustrate applications of the information measures in a computationally intensive context, namely cluster analysis. Information measures for the multivariate normal, Cauchy, and Pareto distributions are presented. Three clustering algorithms are proposed. The algorithms are used to cluster variables and observations in a data set. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Information Theoretic Methods

Dendrograms of clustering nine variables of the automobile data by four linkages.

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Dendrograms of clustering 160 automobiles based on four discrepancy measures.

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