Bootstrap Resampling of Clustering Algorithms
Generate bootstrap replicates of the results of applying a “base” clustering algorithm to a given data set.
cl_boot(x, B, k = NULL,
algorithm = if (is.null(k)) "hclust" else "kmeans",
parameters = list(), resample = FALSE)x |
the data set of objects to be clustered, as appropriate for the base clustering algorithm. |
B |
an integer giving the number of bootstrap replicates. |
k |
|
algorithm |
a character string or function specifying the base clustering algorithm. |
parameters |
a named list of additional arguments to be passed to the base algorithm. |
resample |
a logical indicating whether the data should be
resampled in addition to “sampling from the algorithm”.
If resampling is used, the class memberships of the objects given in
|
This is a rather simple-minded function with limited applicability,
and mostly useful for studying the effect of (uncontrolled) random
initializations of fixed-point partitioning algorithms such as
kmeans or cmeans, see the
examples. To study the effect of varying control parameters or
explicitly providing random starting values, the respective cluster
ensemble has to be generated explicitly (most conveniently by using
replicate to create a list lst of suitable
instances of clusterings obtained by the base algorithm, and using
cl_ensemble(list = lst) to create the ensemble).
A cluster ensemble of length B, with either (if resampling is not used, default) the results of running the base algorithm on the given data set, or (if resampling is used) the memberships for the given data predicted from the results of running the base algorithm on bootstrap samples of the data.
## Study e.g. the effect of random kmeans() initializations.
data("Cassini")
pens <- cl_boot(Cassini$x, 15, 3)
diss <- cl_dissimilarity(pens)
summary(c(diss))
plot(hclust(diss))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.