APappearance-class          package:arules          R Documentation

_C_l_a_s_s "_A_P_a_p_p_e_a_r_a_n_c_e" - _S_p_e_c_i_f_y_i_n_g _t_h_e '_a_p_p_e_a_r_a_n_c_e' _A_r_g_u_m_e_n_t _o_f
_a_p_r_i_o_r_i()

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

     Specifies the restrictions on the associations mined by 'apriori'.
      Note that appearance is not supported by the implementation of
     'eclat'.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     If appearance restrictions are used, an appearance object will be
     created automatically within the 'apriori' function using the
     information in the named list of the function's 'appearance'
     argument.  In this case, the item labels used in the list will be
     automatically matched against the items in the used transaction
     database. The list can contain the following elements:

     '_d_e_f_a_u_l_t': one of '"both"', '"lhs"', '"rhs"', '"none"' (the
          default is '"both"').  This element specified the default
          appearance for all items not  explicitly mentioned in the
          other elements of the list.

     '_l_h_s', '_r_h_s', '_b_o_t_h', '_n_o_n_e', '_i_t_e_m_s': character vectors giving
          the labels of the items which may only appear in the
          corresponding place of the rules/itemsets.

     Objects can also be created by calls of the form 
     'new("APappearance", ...)'.  In this case, item IDs (column
     numbers of the transactions incidence matrix) have to be used
     instead of labels.

_S_l_o_t_s:

     '_s_e_t': an integer scalar indicating how many items are specified
          for each of lhs, rhs, items, both and none

     '_i_t_e_m_s': an integer vector of item IDs (column numbers)

     '_l_a_b_e_l_s': a character vector of item labels

     '_d_e_f_a_u_l_t': a character scalar indicating the value for default
          appearance

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

     Christian Borgelt (2004) _Apriori - Finding Association
     Rules/Hyperedges with the Apriori Algorithm_. <URL:
     http://fuzzy.cs.uni-magdeburg.de/~borgelt/apriori.html>

_S_e_e _A_l_s_o:

     'apriori'

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

     data("Adult")
     ## Mine only rules with small or large income in the right-hand-side.
     rules <- apriori(Adult, parameter = list(confidence = 0.5), 
       appearance = list(rhs = c("income=small", "income=large"),
       default="lhs"))

