nearestNeighborSepVal {clusterGeneration} | R Documentation |
Separation information matrix containing the nearest neighbor and farthest neighbor of each cluster.
nearestNeighborSepVal(sepValMat)
sepValMat |
a |
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, …, 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. |
Weiliang Qiu stwxq@channing.harvard.edu
Harry Joe harry@stat.ubc.ca
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.
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)