coef.hclust             package:cluster             R Documentation

_A_g_g_l_o_m_e_r_a_t_i_v_e _C_o_e_f_f_i_c_i_e_n_t _f_o_r '_h_c_l_u_s_t' _O_b_j_e_c_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computes the "agglomerative coefficient", measuring the clustering
     structure of the dataset.

     For each observation i, denote by m(i) its dissimilarity to the
     first cluster it is merged with, divided by the dissimilarity of
     the merger in the final step of the algorithm.  The agglomerative
     coefficient is the average of all 1 - m(i).  It can also be seen
     as the average width (or the percentage filled) of the banner
     plot.

     Because it grows with the number of observations, this measure
     should not be used to compare datasets of very different sizes.

_U_s_a_g_e:

     coef.hclust(object, ...)
     ## S3 method for class 'hclust':
     coef(object, ...)
     ## S3 method for class 'twins':
     coef(object, ...)

_A_r_g_u_m_e_n_t_s:

  object: an object of class '"hclust"' or '"twins"', i.e., typically
          the result of 'hclust(.)','agnes(.)', or 'diana(.)'.

          Since 'coef.hclust' only uses 'object$heights', and
          'object$merge', 'object' can be any list-like object with
          appropriate 'merge' and 'heights' components. 

     ...: currently unused potential further arguments

_V_a_l_u_e:

     a number specifying the _agglomerative_ (or _divisive_ for 'diana'
     objects) coefficient as defined by Kaufman and Rousseeuw, see
     'agnes.object $ ac' or 'diana.object $ dc'.

_E_x_a_m_p_l_e_s:

     data(agriculture)
     aa <- agnes(agriculture)
     coef(aa) # really just extracts aa$ac
     coef(as.hclust(aa))# recomputes