## S3 method for class 'formula':
specc(x, data = NULL, na.action = na.omit, ...)## S3 method for class 'matrix':
specc(x, centers, kernel = "rbfdot", kpar = list(sigma = 0.1),
nystrom.red = FALSE, nystrom.sample = dim(x)[1]/6, iterations = 200,
mod.sample = 0.75, na.action = na.omit, ...)
## S3 method for class 'kernelMatrix':
specc(x, centers, nystrom.red = FALSE, iterations = 200, ...)
## S3 method for class 'list':
specc(x, centers, kernel = "stringdot", kpar = list(length=4, lambda=0.5),
nystrom.red = FALSE, nystrom.sample = length(x)/6, iterations = 200,
mod.sample = 0.75, na.action = na.omit, ...)
kernelMatrix
, or a list of character vectors."automatic"
uses a heuristic the determine a
suitable value for the width parameter of the RBF kernel.
The second option "l
TRUE
a sample of the dataset is used to calculate the
eigenvalues, thus only a $n x m$ matrix where n the sample size
is stored in memory (default: FALSE
specc
wich extends the class vector
containing integers indicating the cluster to which
each point is allocated. The following slots contain useful informationspecc
function in a matrix
or a
data.frame
, in addition specc
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.kkmeans
, kpca
, kcca
## Cluster the spirals data set.
data(spirals)
sc <- specc(spirals, centers=2)
sc
centers(sc)
size(sc)
withinss(sc)
plot(spirals, col=sc)
Run the code above in your browser using DataLab