partition.object           package:cluster           R Documentation

_P_a_r_t_i_t_i_o_n_i_n_g _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     The objects of class '"partition"' represent a partitioning of a
     dataset into clusters.

_V_a_l_u_e:

     a '"partition"' object is a list with the following (and typically
     more) components: 

clustering: the clustering vector.  An integer vector of length n, the
          number of observations, giving for each observation the
          number (`id') of the cluster to which it belongs.

    call: the matched 'call' generating the object.

 silinfo: a list with all _silhouette_ information, only available when
          the number of clusters is non-trivial, i.e., 1 < k < n and
          then has the following components, see 'silhouette'

          _w_i_d_t_h_s an (n x 3) matrix, as returned by 'silhouette()', with
               for each observation i the cluster to which i belongs,
               as well as the neighbor cluster of i (the cluster, not
               containing i, for which the average dissimilarity
               between its observations and i is minimal), and the
               silhouette width s(i) of the observation.

          _c_l_u_s._a_v_g._w_i_d_t_h_s the average silhouette width per cluster.

          _a_v_g._w_i_d_t_h the average silhouette width for the dataset, i.e.,
               simply the average of s(i) over all observations i.

          This information is also needed to construct a _silhouette
          plot_ of the clustering, see 'plot.partition'.

          Note that 'avg.width' can be maximized over different
          clusterings (e.g. with varying number of clusters) to choose
          an _optimal_ clustering. 

objective: value of criterion maximized during the partitioning
          algorithm, may more than one entry for different stages.

    diss: an object of class '"dissimilarity"', representing the total
          dissimilarity matrix of the dataset (or relevant subset, e.g.
          for 'clara'). 

    data: a matrix containing the original or standardized data.  This
          might be missing to save memory or when a dissimilarity
          matrix was given as input structure to the clustering method. 

_G_E_N_E_R_A_T_I_O_N:

     These objects are returned from 'pam', 'clara' or 'fanny'.

_M_E_T_H_O_D_S:

     The '"partition"' class has a method for the following generic
     functions: 'plot', 'clusplot'.

_I_N_H_E_R_I_T_A_N_C_E:

     The following classes inherit from class '"partition"' : '"pam"',
     '"clara"' and '"fanny"'.

     See 'pam.object', 'clara.object' and 'fanny.object' for details.

_S_e_e _A_l_s_o:

     'pam', 'clara', 'fanny'.