Description Usage Arguments Value References Examples
View source: R/algorithm_sc11Y.R
As a data-driven method, the algorithm recovers geodesic distance from a k-nearest neighbor graph scaled by an (exponential) parameter ρ and applies random-walk spectral clustering. Authors referred their method as density sensitive similarity function.
1 |
data |
an (n\times p) matrix of row-stacked observations or S3 |
k |
the number of clusters (default: 2). |
nnbd |
neighborhood size to define data-driven bandwidth parameter (default: 7). |
rho |
exponent scaling parameter (default: 2). |
... |
extra parameters including
|
a named list of S3 class T4cluster
containing
a length-n vector of class labels (from 1:k).
eigenvalues of the graph laplacian's spectral decomposition.
an (n\times k) low-dimensional embedding.
name of the algorithm.
yang_spectral_2011T4cluster
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # -------------------------------------------------------------
# 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)$Y
## CLUSTERING WITH DIFFERENT K VALUES
cl2 = sc11Y(X, k=2)$cluster
cl3 = sc11Y(X, k=3)$cluster
cl4 = sc11Y(X, k=4)$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="sc11Y: k=2")
plot(X2d, col=cl3, pch=19, main="sc11Y: k=3")
plot(X2d, col=cl4, pch=19, main="sc11Y: k=4")
par(opar)
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