HierarchicalSC {sClust}

Hierarchical Spectral Clustering

Description

Hierarchical Spectral Clustering

Usage

HierarchicalSC(
  W,
  K = 5,
  method = "ward.D2",
  flagDiagZero = FALSE,
  verbose = FALSE
)

Arguments

W

Gram Similarity Matrix.

K

number of cluster to obtain.

method

method that will be used in the hierarchical clustering.

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

References

Sanchez-Garcia, R., Fernnelly, M. and al. (2014). Hierarchical Spectral Clustering of Power Grids. In IEEE Transaction on Power Systems 29.5, pages 2229-2237. ISSN : 0885-8950.

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 <- HierarchicalSC(W,K=2,method = "ward.D2",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 <- HierarchicalSC(W,K=2,method="ward.D2",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='+');