spectralClusteringNg {RclusTool} | R Documentation |
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
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")
[Package RclusTool version 0.91.6 Index]