nearestNeighborSepVal function

SEPARATON INFORMATION MATRIX

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

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.

Returns

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.

Author(s)

Weiliang Qiu weiliang.qiu@gmail.com

Harry Joe harry@stat.ubc.ca

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)