kpcf: Projective k-Means Clustering with Fixed Dimension

In kyoustat/Rsubclust: Subspace Clustering Algorithms

Description

Projective k-Means Clustering with Fixed Dimension

Usage

kpcf(
  X,
  K = 2,
  dims = rep(1, K),
  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
kpcf2 = kpcf(data, K=2, dims=c(1,1))
kpcf3 = kpcf(data, K=3, dims=c(1,1,1))
kpcf4 = kpcf(data, K=4, dims=c(1,1,1,1))

## extract clustering
finc2 = kpcf2$cluster
finc3 = kpcf3$cluster
finc4 = kpcf4$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.