Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)
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Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)

Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s) Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)

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12.07.2015 Views

5.6. Displaying Intrinsically High-Dimensional Data 109Figure 5.7. Local authorities demographic data: Andrews’ curves for threeclusters.

110 5. Graphical Representation of Data Using Principal Componentsits largest coefficients are all positive and correspond to numbers of elderlypersons who have recently moved to the area, numbers in privately rentedaccommodation, and population sparsity (Area/Population). The implicationof the outlying curves for Cluster 12 is that the two local authoritiescorresponding to the curves (Cumbria, Northumberland) have substantiallylarger values for the seventh PC than do the other local authorities in thesame cluster (Cornwall, Gloucestershire, Kent, Lincolnshire, Norfolk, NorthYorkshire, Shropshire, Somerset and Suffolk). This is, indeed, the case andit further implies atypical values for Northumberland and Cumbria, comparedto the remainder of the cluster, for the three variables having thelargest coefficients for the seventh PC.Another example of using Andrews’ curves to examine the homogeneityof clusters in cluster analysis and to investigate potential outliers is givenby Jolliffe et al. (1980) for the data set discussed in Section 4.2.

5.6. Displaying Intrinsically High-Dimensional Data 109Figure 5.7. Local authorities demographic data: Andrews’ curves for threeclusters.

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