kpcv: Projective k-Means Clustering with Varying Dimension

In kyoustat/Rsubclust: Subspace Clustering Algorithms

Description

Projective k-Means Clustering with Varying Dimension

Usage

kpcv(X, K = 2, iter = 496, init = c("kmeans", "random"), print.progress = TRUE)

References

\insertRef

agarwal_k-means_2004Rsubclust

Examples

## generate a toy example of two line components
set.seed(19)
tester = simLP(n=100, nl=2, np=0)
data   = tester$data
label  = tester$class

## do PCA for data reduction
proj = base::eigen(stats::cov(data))$vectors[,1:2]
dat2 = data%*%proj

## run k-plane algorithm with K=2, 3, and 4
kpcv2 = kpcv(data, K=2)
kpcv3 = kpcv(data, K=3)
kpcv4 = kpcv(data, K=4)

## extract clustering
finc2 = kpcv2$cluster
finc3 = kpcv3$cluster
finc4 = kpcv4$cluster

## visualize
opar <- par(mfrow=c(3,4), no.readonly=TRUE)
plot(dat2[,1],dat2[,2],pch=19,cex=0.3,col=finc2+1,main="K=2:PCA")
plot(data[,1],data[,2],pch=19,cex=0.3,col=finc2+1,main="K=2:Axis(1,2)")
plot(data[,1],data[,3],pch=19,cex=0.3,col=finc2+1,main="K=2:Axis(1,3)")
plot(data[,2],data[,3],pch=19,cex=0.3,col=finc2+1,main="K=2:Axis(2,3)")

plot(dat2[,1],dat2[,2],pch=19,cex=0.3,col=finc3+1,main="K=3:PCA")
plot(data[,1],data[,2],pch=19,cex=0.3,col=finc3+1,main="K=3:Axis(1,2)")
plot(data[,1],data[,3],pch=19,cex=0.3,col=finc3+1,main="K=3:Axis(1,3)")
plot(data[,2],data[,3],pch=19,cex=0.3,col=finc3+1,main="K=3:Axis(2,3)")

plot(dat2[,1],dat2[,2],pch=19,cex=0.3,col=finc4+1,main="K=4:PCA")
plot(data[,1],data[,2],pch=19,cex=0.3,col=finc4+1,main="K=4:Axis(1,2)")
plot(data[,1],data[,3],pch=19,cex=0.3,col=finc4+1,main="K=4:Axis(1,3)")
plot(data[,2],data[,3],pch=19,cex=0.3,col=finc4+1,main="K=4:Axis(2,3)")
par(opar)

kyoustat/Rsubclust documentation built on Feb. 22, 2020, 12:20 a.m.