compute.kclust {sClust}

Gram similarity matrix checker

Description

Function which select the number of cluster to compute thanks to a selected method

Usage

compute.kclust(
  eigenValues,
  method = "default",
  Kmax = 20,
  tolerence = 1,
  threshold = 0.9,
  verbose = FALSE
)

Arguments

eigenValues

The eigenvalues of the laplacian matrix.

method

The method that will be used. "default" to let the function choose the most suitable method. "PEV" for the Principal EigenValue method. "GAP" for the GAP method.

Kmax

The maximum number of cluster which is allowed.

tolerence

The tolerance allowed for the Principal EigenValue method.

threshold

The threshold to select the dominant eigenvalue for the GAP method.

verbose

To output the verbose in the terminal.

Value

a vector which contain the number of cluster to compute.

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))
W <- checking.gram.similarityMatrix(W)
eigVal <- compute.laplacian.NJW(W,verbose = TRUE)$eigen$values
K <- compute.kclust(eigVal, method="default", Kmax=20, tolerence=0.99, threshold=0.9, verbose=TRUE)

### Example 2: Speed and Stopping Distances of Cars
W <- compute.similarity.ZP(scale(cars))
W <- checking.gram.similarityMatrix(W)
eigVal <- compute.laplacian.NJW(W,verbose = TRUE)$eigen$values
K <- compute.kclust(eigVal, method="default", Kmax=20, tolerence=0.99, threshold=0.9, verbose=TRUE)