UnormalizedSC: Unormalized Spectral Clustering Ng.

In sClust: R Toolbox for Unsupervised Spectral Clustering

Description

The function, for a given similarity matrix, will separate the data using a spectral space. It does not normalize the Laplacian matrix compared to other algorithms

Usage

UnormalizedSC(W, K = 5, flagDiagZero = FALSE, verbose = FALSE)

Arguments

W	Gram Similarity Matrix.
K	number of cluster to obtain.
flagDiagZero	if True, Put zero on the similarity matrix W.
verbose	To output the verbose in the terminal.

Value

returns a list containing the following elements:

• cluster: a vector containing the cluster

• eigenVect: a vector containing the eigenvectors

• eigenVal: a vector containing the eigenvalues

Author(s)

Emilie Poisson Caillault and Erwan Vincent

Examples

### Example 1: 2 disks of the same size
n<-100 ; r1<-1
x<-(runif(n)-0.5)*2;
y<-(runif(n)-0.5)*2
keep1<-which((x*2+y*2)<(r1*2))
disk1<-data.frame(x+3*r1,y)[keep1,]
disk2 <-data.frame(x-3*r1,y)[keep1,]
sameTwoDisks <- rbind(disk1,disk2)
W <- compute.similarity.ZP(scale(sameTwoDisks))
res <- UnormalizedSC(W,K=2,flagDiagZero=TRUE,verbose=TRUE)
plot(sameTwoDisks, col = res$cluster)
plot(res$eigenVect[,1:2], col = res$cluster, main="spectral space",
     xlim=c(-1,1),ylim=c(-1,1)); points(0,0,pch='+');
plot(res$eigenVal, main="Laplacian eigenvalues",pch='+'); 

### Example 2: Speed and Stopping Distances of Cars
W <- compute.similarity.ZP(scale(iris[,-5]))
res <- UnormalizedSC(W,K=2,flagDiagZero=TRUE,verbose=TRUE)
plot(iris, col = res$cluster)
plot(res$eigenVect[,1:2], col = res$cluster, main="spectral space",
     xlim=c(-1,1),ylim=c(-1,1)); points(0,0,pch='+');
plot(res$eigenVal, main="Laplacian eigenvalues",pch='+'); 

sClust documentation built on Aug. 24, 2021, 1:06 a.m.