nearestNeighborSepVal   package:clusterGeneration   R Documentation

_S_E_P_A_R_A_T_O_N _I_N_F_O_R_M_A_T_I_O_N _M_A_T_R_I_X

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

       nearestNeighborSepVal(sepValMat)

_A_r_g_u_m_e_n_t_s:

sepValMat: a $K\times K$ matrix, where $K$ is the number of clusters. 
          'sepValMat[i,j]' is the separation index between cluster $i$
          and $j$. 

_V_a_l_u_e:

     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. 

_A_u_t_h_o_r(_s):

     Weiliang Qiu stwxq@channing.harvard.edu
      Harry Joe harry@stat.ubc.ca

_R_e_f_e_r_e_n_c_e_s:

     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.

_E_x_a_m_p_l_e_s:

     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)

