sc12L {T4cluster}

Spectral Clustering by Li and Guo (2012)

Description

Li and Guo proposed to construct an affinity matrix

A_{ij} = \exp(-d(x_i, d_j)^2 / 2 \sigma^2)

and adjust the matrix by neighbor propagation. Then, standard spectral clustering from the symmetric, normalized graph laplacian is applied.

Usage

sc12L(data, k = 2, sigma = 1, ...)

Arguments

data

an (n\times p) matrix of row-stacked observations or S3 dist object of n observations.

k

the number of clusters (default: 2).

sigma

common bandwidth parameter (default: 1).

...

extra parameters including

algclust

method to perform clustering on embedded data; either "kmeans" (default) or "GMM".

maxiter

the maximum number of iterations (default: 10).

Value

a named list of S3 class T4cluster containing

cluster

a length-n vector of class labels (from 1:k).

eigval

eigenvalues of the graph laplacian's spectral decomposition.

embeds

an (n\times k) low-dimensional embedding.

algorithm

name of the algorithm.

References

Li X, Guo L (2012). “Constructing Affinity Matrix in Spectral Clustering Based on Neighbor Propagation.” Neurocomputing, 97, 125–130. ISSN 09252312.

See Also

scNJW

Examples

# -------------------------------------------------------------
#            clustering with 'iris' dataset
# -------------------------------------------------------------
## PREPARE
data(iris)
X   = as.matrix(iris[,1:4])
lab = as.integer(as.factor(iris[,5]))

## EMBEDDING WITH PCA
X2d = Rdimtools::do.pca(X, ndim=2)$Y

## CLUSTERING WITH DIFFERENT K VALUES
cl2 = sc12L(X, k=2)$cluster
cl3 = sc12L(X, k=3)$cluster
cl4 = sc12L(X, k=4)$cluster

## VISUALIZATION
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,4), pty="s")
plot(X2d, col=lab, pch=19, main="true label")
plot(X2d, col=cl2, pch=19, main="sc12L: k=2")
plot(X2d, col=cl3, pch=19, main="sc12L: k=3")
plot(X2d, col=cl4, pch=19, main="sc12L: k=4")
par(opar)