bipartitionShi: 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 Shi and Malik, 2000).

Usage

bipartitionShi(sim)

Arguments

sim	similarity matrix.

Details

bipartitionShi returns a partition obtained by spectral clustering (according to Shi and Malik, 2000)

Value

The function returns a list containing:

label	vector of labels.
eigenvector	matrix containing, in columns, the eigenvectors of the similarity matrix.
eigenvalue	vector containing the eigenvalues of the similarity matrix.

References

J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8), 888-905.

Examples

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

sim <- computeGaussianSimilarity(dat, 1)
res <- bipartitionShi(sim)

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

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