Description Usage Arguments Value References Examples
k-means++ algorithm is known to be a smart, careful initialization technique. It is originally intended to return a set of k points as initial centers though it can still be used as a rough clustering algorithm by assigning points to the nearest points.
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data |
an (n\times p) matrix whose rows are observations. |
k |
the number of clusters. |
a length-n vector of class labels.
arthur_kmeans_2007maotai
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ## use simple example of iris dataset
data(iris)
mydata = as.matrix(iris[,1:4])
mycol = as.factor(iris[,5])
## find the low-dimensional embedding for visualization
my2d = cmds(mydata, ndim=2)$embed
## apply 'kmeanspp' with different numbers of k's.
k2 = kmeanspp(mydata, k=2)
k3 = kmeanspp(mydata, k=3)
k4 = kmeanspp(mydata, k=4)
k5 = kmeanspp(mydata, k=5)
k6 = kmeanspp(mydata, k=6)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,3))
plot(my2d, col=k2, main="k=2", pch=19, cex=0.5)
plot(my2d, col=k3, main="k=3", pch=19, cex=0.5)
plot(my2d, col=k4, main="k=4", pch=19, cex=0.5)
plot(my2d, col=k5, main="k=5", pch=19, cex=0.5)
plot(my2d, col=k6, main="k=6", pch=19, cex=0.5)
plot(my2d, col=mycol, main="true cluster", pch=19, cex=0.5)
par(opar)
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