kernlab (version 0.6-2)

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, ...)

Arguments

x

the matrix of data to be clustered or a symbolic description of the model to be fit.

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 dot product between two vector arguments. kernlab provides the most popular kernel functions which can be used by

kpar

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.

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)