## S3 method for class 'formula':
kkmeans(x, data = NULL, na.action = na.omit, ...)## S3 method for class 'matrix':
kkmeans(x, centers, kernel = "rbfdot", kpar = "automatic",
alg="kkmeans", p=1, na.action = na.omit, ...)
## S3 method for class 'kernelMatrix':
kkmeans(x, centers, ...)
## S3 method for class 'list':
kkmeans(x, centers, kernel = "stringdot",
kpar = list(length=4, lambda=0.5),
alg ="kkmeans", p = 1, na.action = na.omit, ...)
kernelMatrix
, or a list of character vectors.link{kernels}
). "automatic"
uses a heuristic the determine a
suitable value for the width parameter of the RBF kernel.
A list can also be used contaikkmeans
and kerninghan
.specc
which extends the class vector
containing integers indicating the cluster to which
each point is allocated. The following slots contain useful informationkernel k-means
uses the 'kernel trick' (i.e. implicitly projecting all data
into a non-linear feature space with the use of a kernel) in order to
deal with one of the major drawbacks of k-means
that is that it cannot
capture clusters that are not linearly separable in input space.
The algorithm is implemented using the triangle inequality to avoid
unnecessary and computational expensive distance calculations.
This leads to significant speedup particularly on large data sets with
a high number of clusters.
With a particular choice of weights this algorithm becomes
equivalent to Kernighan-Lin, and the norm-cut graph partitioning
algorithms.
The function also support input in the form of a kernel matrix
or a list of characters for text clustering.
The data can be passed to the kkmeans
function in a matrix
or a
data.frame
, in addition kkmeans
also supports input in the form of a
kernel matrix of class kernelMatrix
or as a list of character
vectors where a string kernel has to be used.specc
, kpca
, kcca
## Cluster the iris data set.
data(iris)
sc <- kkmeans(as.matrix(iris[,-5]), centers=3)
sc
centers(sc)
size(sc)
withinss(sc)
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