spectralClusteringNg: Spectral clustering

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

Spectral clustering

Description

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

Usage

spectralClusteringNg(sim, K)

Arguments

sim	similarity matrix.
K	number of clusters.

Details

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

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.

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")

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