"Face-shaped" clustered benchmark datasets
Generates "face-shaped" clustered benchmark datasets. This is based on a collaboration with Martin Maechler.
rFace(n, p = 6, nrep.top = 2, smile.coef = 0.6, dMoNo = 1.2, dNoEy = 1)
n |
integer greater or equal to 10. Number of points. |
p |
integer greater or equal to 2. Dimension. |
nrep.top |
integer. Number of repetitions of the hair-top point. |
smile.coef |
numeric. Coefficient for quadratic term used for generation of mouth-points. Positive values=>smile. |
dMoNo |
number. Distance from mouth to nose. |
dNoEy |
number. Minimum vertical distance from mouth to eyes. |
The function generates a nice benchmark example for cluster
analysis.
There are six "clusters" in this data, of which the first five are
clearly homogeneous patterns, but with different distributional
shapes and different qualities of separation. The clusters are
distinguished only in the first two dimensions. The attribute
grouping is a factor giving the cluster numbers, see below.
The sixth group of
points corresponds to some hairs, and is rather a collection of
outliers than a cluster in itself. This group contains
nrep.top+2 points. Of the remaining points, 20% belong to
cluster 1, the chin (quadratic function plus noise).
10% belong to cluster 2, the right eye (Gaussian). 30% belong to
cluster 3, the mouth (Gaussian/squared Gaussian).
20% belong to cluster 4, the nose (Gaussian/gamma), and
20% belong to cluster 5, the left eye (uniform).
The distributions of the further variables are homogeneous over all points. The third dimension is exponentially distributed, the fourth dimension is Cauchy distributed, all further distributions are Gaussian.
Please consider the source code for exact generation of the clusters.
An n times p numeric matrix with attributes
grouping |
a factor giving the cluster memberships of the points. |
indexlist |
a list of six vectors containing the indices of points belonging to the six groups. |
set.seed(4634) face <- rFace(600,dMoNo=2,dNoEy=0) grface <- as.integer(attr(face,"grouping")) plot(face, col = grface) # pairs(face, col = grface, main ="rFace(600,dMoNo=2,dNoEy=0)")
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