diana.object             package:cluster             R Documentation

_D_i_v_i_s_i_v_e _A_n_a_l_y_s_i_s (_D_I_A_N_A) _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     The objects of class '"diana"' represent a divisive hierarchical
     clustering of a dataset.

_V_a_l_u_e:

     A legitimate 'diana' object is a list with the following
     components: 

   order: a vector giving a permutation of the original observations to
          allow for plotting, in the sense that the branches of a
          clustering tree will not cross. 

order.lab: a vector similar to 'order', but containing observation
          labels instead of observation numbers.  This component is
          only available if the original observations were labelled. 

  height: a vector with the diameters of the clusters prior to
          splitting. 

      dc: the divisive coefficient, measuring the clustering structure
          of the dataset.  For each observation i, denote by d(i) the
          diameter of the last cluster to which it belongs (before
          being split off as a single observation), divided by the
          diameter of the whole dataset.  The 'dc' is the average of
          all 1 - d(i).  It can also be seen as the average width (or
          the percentage filled) of the banner plot. Because 'dc' grows
          with the number of observations, this measure should not be
          used to compare datasets of very different sizes. 

   merge: an (n-1) by 2 matrix, where n is the number of observations.
          Row i of 'merge' describes the split at step n-i of the
          clustering. If a number j in row r is negative, then the
          single observation |j| is split off at stage n-r. If j is
          positive, then the cluster that will be splitted at stage n-j
          (described by row j), is split off at stage n-r. 

    diss: an object of class '"dissimilarity"', representing the total
          dissimilarity matrix of the dataset. 

    data: a matrix containing the original or standardized
          measurements, depending on the 'stand' option of the function
          'agnes'.  If a dissimilarity matrix was given as input
          structure, then this component is not available. 

_G_E_N_E_R_A_T_I_O_N:

     This class of objects is returned from 'diana'.

_M_E_T_H_O_D_S:

     The '"diana"' class has methods for the following generic
     functions: 'print', 'summary', 'plot'.

_I_N_H_E_R_I_T_A_N_C_E:

     The class '"diana"' inherits from '"twins"'. Therefore, the
     generic function 'pltree' can be used on a 'diana' object, and an
     'as.hclust' method is available.

_S_e_e _A_l_s_o:

     'agnes', 'diana', 'plot.diana', 'twins.object'.

_E_x_a_m_p_l_e_s:

     ## really see example(diana) !   Additionally:
     data(votes.repub)
     dv0 <- diana(votes.repub, stand = TRUE)
     ## Cut into 2 groups:
     dv2 <- cutree(as.hclust(dv0), k = 2)
     table(dv2)
     rownames(votes.repub)[dv2 == 1]

