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IDEAL
2005
Springer

Cluster Analysis of High-Dimensional Data: A Case Study

10 years 6 months ago
Cluster Analysis of High-Dimensional Data: A Case Study
Abstract. Normal mixture models are often used to cluster continuous data. However, conventional approaches for fitting these models will have problems in producing nonsingular estimates of the componentcovariance matrices when the dimension of the observations is large relative to the number of observations. In this case, methods such as principal components analysis (PCA) and the mixture of factor analyzers model can be adopted to avoid these estimation problems. We examine these approaches applied to the Cabernet wine data set of Ashenfelter (1999), considering the clustering of both the wines and the judges, and comparing our results with another analysis. The mixture of factor analyzers model proves particularly effective in clustering the wines, accurately classifying many of the wines by location.
Richard Bean, Geoffrey J. McLachlan
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where IDEAL
Authors Richard Bean, Geoffrey J. McLachlan
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