sc10Z {T4cluster}

Spectral Clustering by Zhang et al. (2010)

Description

The algorithm defines a set of data-driven bandwidth parameters p_{ij} by constructing a similarity matrix. Then the affinity matrix is defined as

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

and the standard spectral clustering of Ng, Jordan, and Weiss (scNJW) is applied.

Usage

sc10Z(data, k = 2, ...)

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).

...

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

Zhang Y, Zhou J, Fu Y (2010). “Spectral Clustering Algorithm Based on Adaptive Neighbor Distance Sort Order.” In The 3rd International Conference on Information Sciences and Interaction Sciences, 444–447. ISBN 978-1-4244-7384-7.

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 = sc10Z(X, k=2)$cluster
cl3 = sc10Z(X, k=3)$cluster
cl4 = sc10Z(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="sc10Z: k=2")
plot(X2d, col=cl3, pch=19, main="sc10Z: k=3")
plot(X2d, col=cl4, pch=19, main="sc10Z: k=4")
par(opar)