kernlab (version 0.8-2)

kkmeans: Kernel k-means

Description

A weigthed kernel version of the famous k-means algorithm.

Usage

## S3 method for class 'formula':
kkmeans(x, data = NULL, na.action = na.omit, ...)
## S3 method for class 'matrix':
kkmeans(x, centers, kernel = "rbfdot", kpar = list(sigma = 0.1),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, ...)

Arguments

x

the matrix of data to be clustered, or a symbolic description of the model to be fit, or a kernel Matrix of class kernelMatrix, or a list of character vectors.

data

an optional data frame containing the variables in the model. By default the variables are taken from the environment which `kkmeans' is called from.

centers

Either the number of clusters or a set of initial cluster centers. If the first, a random set of rows in the eigenvectors matrix are chosen as the initial centers.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a inner product in feature space between two vector arguments (see link{kernels}). kernlab

kpar

a character string or the list of hyper-parameters (kernel parameters). The default character string "automatic" uses a heuristic the determine a suitable value for the width parameter of the RBF kernel. A list can also be used contai

alg

the algorithm to use. Options currently include kkmeans and kerninghan.

p

a parameter used to keep the affinity matrix positive semidefinite

na.action

The action to perform on NA

...

additional parameters

Value

• An S4 object of class specc wich extends the class vector containing integers indicating the cluster to which each point is allocated. The following slots contain useful information
• centers
  A matrix of cluster centers.
• size
  The number of point in each cluster
• withinss
  The within-cluster sum of squares for each cluster
• kernelf
  The kernel function used

Details

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.

References

Inderjit Dhillon, Yuqiang Guan, Brian Kulis A Unified view of Kernel k-means, Spectral Clustering and Graph Partitioning UTCS Technical Report http://www.cs.utexas.edu/users/kulis/pubs/spectral_techreport.pdf

See Also

specc, kpca, kcca

Examples

## Cluster the iris data set.
data(iris)

sc <- kkmeans(as.matrix(iris[,-5]), centers=3)

sc
centers(sc)
size(sc)
withinss(sc)