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The clustergram: A graph for visualizing hierarchical and nonhierarchical cluster analyses AgEcon
Schonlau, Matthias.
In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propose an alternative graph called a “clustergram” to examine how cluster members are assigned to clusters as the number of clusters increases. This graph is useful in exploratory analysis for nonhierarchical clustering algorithms such as k-means and for hierarchical cluster algorithms when the number of observations is large enough to make dendrograms impractical. I present the Stata code and give two examples.
Tipo: Journal Article Palavras-chave: Dendrogram; Tree; Clustering; Nonhierarchical; Large data; Asbestos; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/116024
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Boosted regression (boosting): An introductory tutorial and a Stata plugin AgEcon
Schonlau, Matthias.
Boosting, or boosted regression, is a recent data-mining technique that has shown considerable success in predictive accuracy. This article gives an overview of boosting and introduces a new Stata command, boost, that implements the boosting algorithm described in Hastie, Tibshirani, and Friedman (2001, 322). The plugin is illustrated with a Gaussian and a logistic regression example. In the Gaussian regression example, the R2 value computed on a test dataset is R2 = 21.3% for linear regression and R2 = 93.8% for boosting. In the logistic regression example, stepwise logistic regression correctly classifies 54.1% of the observations in a test dataset versus 76.0% for boosted logistic regression. Currently, boost accommodates Gaussian (normal), logistic,...
Tipo: Journal Article Palavras-chave: Boost; Boosted regression; Boosting; Data mining; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117524
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