kernlab (version 0.1-4)

specc: Spectral Clustering

Description

A spectral clustering algorithm. This algorithm clusters points using eigenvectors of kernel matrixes derived from the data.

Usage

## 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), iterations = 200, 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 `specc' 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 se

kpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. For valid parameters for existing kernels are :

• sigma(inverse kernel width for the Radial

iterations

The maximum number of iterations allowed.

na.action

The action to perform on NA

...

additional parameters

Value

• An S4 object of class specc
• cluster
  A vector of integers indicating the cluster to which each point is allocated.
• centers
  A matrix of cluster centers.
• size
  The number of point in each cluster

Details

In Spectral Clustering one uses the top k (number of clusters) eigenvectors of a matrix derived from the distance between points. Very good results are obtained by using a standard clustering technique to cluster the resulting eigenvector matrixes.

References

Andrew Y. Ng, Michael I. Jordan, Yair Weiss On Spectral Clustering: Analysis and an Algorithm Neural Information Processing Symposium 2001 http://www.nips.cc/NIPS2001/papers/psgz/AA35.ps.gz

See Also

kpca, kcca

Examples

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

sc <- specc(spirals, centers=2, kernel="rbfdot", kpar=list(sigma=400))

centers(sc)
size(sc)

plot(spirals, col=cluster(sc))