sc05Z {T4cluster} | R Documentation |
Spectral Clustering by Zelnik-Manor and Perona (2005)
Description
Zelnik-Manor and Perona proposed a method to define a set of data-driven
bandwidth parameters where \sigma_i
is the distance from a point x_i
to its nnbd
-th
nearest neighbor. Then the affinity matrix is defined as
A_{ij} = \exp(-d(x_i, d_j)^2 / \sigma_i \sigma_j)
and the standard
spectral clustering of Ng, Jordan, and Weiss (scNJW
) is applied.
Usage
sc05Z(data, k = 2, nnbd = 7, ...)
Arguments
data |
an |
k |
the number of clusters (default: 2). |
nnbd |
neighborhood size to define data-driven bandwidth parameter (default: 7). |
... |
extra parameters including
|
Value
a named list of S3 class T4cluster
containing
- cluster
a length-
n
vector of class labels (from1:k
).- eigval
eigenvalues of the graph laplacian's spectral decomposition.
- embeds
an
(n\times k)
low-dimensional embedding.- algorithm
name of the algorithm.
References
Zelnik-manor L, Perona P (2005). “Self-Tuning Spectral Clustering.” In Saul LK, Weiss Y, Bottou L (eds.), Advances in Neural Information Processing Systems 17, 1601–1608. MIT Press.
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)$Y
## CLUSTERING WITH DIFFERENT K VALUES
cl2 = sc05Z(X, k=2)$cluster
cl3 = sc05Z(X, k=3)$cluster
cl4 = sc05Z(X, k=4)$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="sc05Z: k=2")
plot(X2d, col=cl3, pch=19, main="sc05Z: k=3")
plot(X2d, col=cl4, pch=19, main="sc05Z: k=4")
par(opar)