RclusTool (version 0.91.61)

spectralClusteringNg: Spectral clustering

Description

Perform spectral clustering thanks to a similarity matrix (according to Ng et al., 2002).

Usage

spectralClusteringNg(sim, K)

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.

Arguments

sim

similarity matrix.

K

number of clusters.

Details

spectralClusteringNg returns a partition obtained by spectral clustering (according to Ng et al., 2002)

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

spectralClustering

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 <- spectralClusteringNg(sim, K=3)

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