nearestNeighborSepVal {clusterGeneration}

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.

Author(s)

Weiliang Qiu [e-mail]
Harry Joe [e-mail]

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

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