boot                  package:clue                  R Documentation

_B_o_o_t_s_t_r_a_p _R_e_s_a_m_p_l_i_n_g _o_f _C_l_u_s_t_e_r_i_n_g _A_l_g_o_r_i_t_h_m_s

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

     Generate bootstrap replicates of the results of applying a "base"
     clustering algorithm to a given data set.

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

     cl_boot(x, B, k = NULL,
             algorithm = if (is.null(k)) "hclust" else "kmeans", 
             parameters = list(), resample = FALSE)

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

       x: the data set of objects to be clustered, as appropriate for
          the base clustering algorithm.

       B: an integer giving the number of bootstrap replicates.

       k: 'NULL' (default), or an integer giving the number of classes
          to be used for a partitioning base algorithm.

algorithm: a character string or function specifying the base
          clustering algorithm.

parameters: a named list of additional arguments to be passed to the
          base algorithm.

resample: a logical indicating whether the data should be resampled in
          addition to "sampling from the algorithm". Currently, only
          'FALSE' is supported, and an error is given otherwise.

_D_e_t_a_i_l_s:

     This is a rather simple-minded function with limited
     applicability, and mostly useful for studying the effect of
     (uncontrolled) random initializations of fixed-point partitioning
     algorithms such as 'kmeans' or 'cmeans', see the examples.  To
     study the effect of varying control parameters or explicitly
     providing random starting values, the respective cluster ensemble
     has to be generated explicitly (most conveniently by using
     'replicate' to create a list 'lst' of suitable instances of
     clusterings obtained by the base algorithm, and using
     'cl_ensemble(list = lst)' to create the ensemble).

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

     A cluster ensemble with the results of the B runs of the base
     algorithm on the data set.

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

     ## Study e.g. the effect of random kmeans() initializations.
     data("Cassini")
     pens <- cl_boot(Cassini$x, 15, 3)
     diss <- cl_dissimilarity(pens)
     summary(c(diss))
     plot(hclust(diss))

