scUL {T4cluster} | R Documentation |
Spectral Clustering with Unnormalized Laplacian
Description
The version of Shi and Malik first constructs the affinity matrix
A_{ij} = \exp(-d(x_i, d_j)^2 / \sigma^2)
where \sigma
is a common bandwidth parameter and performs k-means (or possibly, GMM) clustering on
the row-space of eigenvectors for the unnormalized graph laplacian matrix
L=D-A
.
Usage
scUL(data, k = 2, sigma = 1, ...)
Arguments
data |
an |
k |
the number of clusters (default: 2). |
sigma |
bandwidth parameter (default: 1). |
... |
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
von Luxburg U (2007). “A Tutorial on Spectral Clustering.” Statistics and Computing, 17(4), 395–416. ISSN 0960-3174, 1573-1375.
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)$Y
## CLUSTERING WITH DIFFERENT K VALUES
cl2 = scUL(X, k=2)$cluster
cl3 = scUL(X, k=3)$cluster
cl4 = scUL(X, k=4)$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="scUL: k=2")
plot(X2d, col=cl3, pch=19, main="scUL: k=3")
plot(X2d, col=cl4, pch=19, main="scUL: k=4")
par(opar)