kmeans {T4cluster} | R Documentation |
K-Means Clustering
Description
K
-means algorithm we provide is a wrapper to the Armadillo's k-means routine.
Two types of initialization schemes are employed. Please see the parameters section for more details.
Usage
kmeans(data, k = 2, ...)
Arguments
data |
an |
k |
the number of clusters (default: 2). |
... |
extra parameters including
|
Value
a named list of S3 class T4cluster
containing
- cluster
a length-
n
vector of class labels (from1:k
).- mean
a
(k\times p)
matrix where each row is a class mean.- wcss
within-cluster sum of squares (WCSS).
- algorithm
name of the algorithm.
References
Sanderson C, Curtin R (2016). “Armadillo: A Template-Based C++ Library for Linear Algebra.” The Journal of Open Source Software, 1(2), 26. ISSN 2475-9066.
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)$Y
## CLUSTERING WITH DIFFERENT K VALUES
cl2 = kmeans(X, k=2)$cluster
cl3 = kmeans(X, k=3)$cluster
cl4 = kmeans(X, k=4)$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="k-means: k=2")
plot(X2d, col=cl3, pch=19, main="k-means: k=3")
plot(X2d, col=cl4, pch=19, main="k-means: k=4")
par(opar)