clusterGeneration (version 1.3.4)

nearestNeighborSepVal: SEPARATON INFORMATION MATRIX

Description

Separation information matrix containing the nearest neighbor and farthest neighbor of each cluster.

Usage

nearestNeighborSepVal(sepValMat)

Arguments

sepValMat

a K by K matrix, where K is the number of clusters. sepValMat[i,j] is the separation index between cluster i and j.

Value

This function returns a separation information matrix containing K rows and the following six columns, where K is the number of clusters.

Column 1:

Labels of clusters (\(1, 2, \ldots, numClust\)), where \(numClust\) is the number of clusters for the data set.

Column 2:

Labels of the corresponding nearest neighbors.

Column 3:

Separation indices of the clusters to their nearest neighboring clusters.

Column 4:

Labels of the corresponding farthest neighboring clusters.

Column 5:

Separation indices of the clusters to their farthest neighbors.

Column 6:

Median separation indices of the clusters to their neighbors.

References

Qiu, W.-L. and Joe, H. (2006a) Generation of Random Clusters with Specified Degree of Separaion. Journal of Classification, 23(2), 315-334.

Qiu, W.-L. and Joe, H. (2006b) Separation Index and Partial Membership for Clustering. Computational Statistics and Data Analysis, 50, 585--603.

Examples

# NOT RUN {
n1<-50
mu1<-c(0,0)
Sigma1<-matrix(c(2,1,1,5),2,2)
n2<-100
mu2<-c(10,0)
Sigma2<-matrix(c(5,-1,-1,2),2,2)
n3<-30
mu3<-c(10,10)
Sigma3<-matrix(c(3,1.5,1.5,1),2,2)

projDir<-c(1, 0)
muMat<-rbind(mu1, mu2, mu3)
SigmaArray<-array(0, c(2,2,3))
SigmaArray[,,1]<-Sigma1
SigmaArray[,,2]<-Sigma2
SigmaArray[,,3]<-Sigma3

tmp<-getSepProjTheory(muMat, SigmaArray, iniProjDirMethod="SL")
sepValMat<-tmp$sepValMat
nearestNeighborSepVal(sepValMat)
# }