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10 30 50 70 0 10 20
80
70
Sepal length (mm) 60
50
40
70
50
Petal length (mm)
30
10
45
40
35
Sepal width (mm)
30
25
20
25
20
15
Petal width (mm)
10
5
0
40 50 60 70 80 20 30 40
Iris Species Setosa Versicolor Virginica

Figure 2.15 Visualization of the Iris data set using a scatter-plot matrix. Source: http://support.sas.com/
documentation/cdl/en/grstatproc/61948/HTML/default/images/gsgscmat.gif .

Viewing large tables of data can be tedious. By condensing the data, Chernoff faces
make the data easier for users to digest. In this way, they facilitate visualization of reg-
ularities and irregularities present in the data, although their power in relating multiple
relationships is limited. Another limitation is that specific data values are not shown.
Furthermore, facial features vary in perceived importance. This means that the similarity
of two faces (representing two multidimensional data points) can vary depending on the
order in which dimensions are assigned to facial characteristics. Therefore, this mapping
should be carefully chosen. Eye size and eyebrow slant have been found to be important.
Asymmetrical Chernoff faces were proposed as an extension to the original technique.
Since a face has vertical symmetry (along the y-axis), the left and right side of a face are
identical, which wastes space. Asymmetrical Chernoff faces double the number of facial
characteristics, thus allowing up to 36 dimensions to be displayed.
The stick figure visualization technique maps multidimensional data to five-piece
stick figures, where each figure has four limbs and a body. Two dimensions are mapped
to the display (x and y) axes and the remaining dimensions are mapped to the angle