spectralClustering: Spectral clustering

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

Spectral clustering

Description

Perform spectral clustering thanks to a similarity matrix.

Usage

spectralClustering(
  sim,
  K,
  nruns = 1,
  test.dim = FALSE,
  projection = "Ng",
  post.norm = "sphere",
  clustering = "kmeans",
  max.dim = 30,
  add.1 = FALSE
)

Arguments

sim	similarity matrix.
K	number of clusters.
nruns	number of random sets.
test.dim	boolean, only used to test dimensions (setting the number of left singular vectors to be computed).
projection	character: final data projection. Must be 'Ng' (default), 'LPP', or 'AFC'.
post.norm	character: final data normalization. Must be 'sphere' (default) or 'Shi'.
clustering	character: clustering method in the spectral space. Must be 'kmeans' (default) or 'pam'.
max.dim	maximal number of dimensions for the computation of left sigular vectors.
add.1	boolean : if TRUE add a column to the matrix containing the eigenvectors of the similarity matrix equal to 1/sqrt(nrow(similarity matrix))

Details

spectralClustering returns a partition obtained by spectral clustering

Value

The function returns a list containing:

label	vector of labels.
medoids	matrix of cluster centers in the space of the K first normalized eigenvectors.
id.med	vector containing the medoids indices.
x	matrix containing, in columns, the eigenvectors of the similarity matrix.
eigen.val	vector containing the eigenvalues of the similarity matrix.
cluster.info	some statistics on each cluster.
ncp	number of left singular vectors computed.

References

A. Ng, M. Jordan, Y. Weiss, On spectral clustering: Analysis and an algorithm, Neural Inf. Process. Systems NIPS14 (2002), pp. 849-856.

See Also

spectralClusteringNg

Examples

dat <- rbind(matrix(rnorm(100, mean = 0, sd = 0.3), ncol = 2), 
           matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
           matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2))

sim <- computeGaussianSimilarity(dat, 1)
res <- spectralClustering(sim, K=3)

plot(dat[,1], dat[,2], type = "p", xlab = "x", ylab = "y", 
col = res$label, main = "Initial features space")
plot(res$x[,1], res$x[,2], type = "p", xlab = "2nd eigenvector", 
	ylab = "3rd eigenvector", col = res$label, main = "Spectral embedding")

RclusTool documentation built on Aug. 29, 2022, 9:07 a.m.