ASparameter-class           package:arules           R Documentation

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

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

     The 'ASparameter' class holds the mining parameters (e.g., minimum
     support) for the used mining algorithms. 'APparameter' and
     'ECparameter' directly extend 'ASparameter' with additional slots
     for parameters only suitable for the Apriori ('APparameter') or
     the Eclat algorithms ('ECparameter').

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

     A suitable default parameter object will be automatically created
     by the 'apriori' or the 'eclat' function.  By specifying a named
     list (names equal to slots) as 'parameter' argument for the
     'apriori' or the  'eclat' function, default values can be replaced
     by the values in the list. Objects can be created by calls of the
     form 'new("APparameter", ...)' or 'new("ECparameter", ...)'.

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

     Common slots defined in 'ASparameter':

     '_s_u_p_p_o_r_t': '"numeric"'; minimal support of an item set (default:
          0.1)

     '_m_i_n_l_e_n': '"integer"'; minimal number of items per item set
          (default: 1)

     '_m_a_x_l_e_n': '"integer"'; maximal number of items per item set
          (default: 5)

     '_t_a_r_g_e_t': '"character"'; indicates the type of association mined.
          One of

             *  '"frequent itemsets"' 

             *  '"maximally frequent itemsets"'

             *  '"closed itemsets"'

             *  '"rules"' (only available for Apriori) 

             *  '"hyperedgesets"' (only available for Apriori;  see
                references for the definition of association
                hyperegesets)


     '_e_x_t': '"logical"'; produce extended information on quality
          measures (e.g., lhs.support) (default: 'FALSE')

     Additional slots for Apriori in 'APparameter':

     '_c_o_n_f_i_d_e_n_c_e': '"numeric"'; minimal confidence of rules/association
          hyperedges (default: 0.8)

     '_s_m_a_x': '"numeric"'; maximal support of
          itemsets/rules/hyperedgesets (default: 1)

     '_a_r_e_m': '"character"'; additional rule evaluation measure
          (default: none) given by one of

          '"_n_o_n_e"': no additional evaluation measure

          '"_d_i_f_f"': absolute confidence difference

          '"_q_u_o_t"': difference of confidence quotient to 1

          '"_a_i_m_p"': absolute difference of improvement to 1

          '"_i_n_f_o"': information difference to prior

          '"_c_h_i_2"': normalized chi^2 measure


     '_a_v_a_l': '"logical"'; return the additional rule evaluation measure
          selected with 'arem'.

     '_m_i_n_v_a_l': '"numeric"'; minimal value of additional evaluation
          measure selected with 'arem' (default: 0.1)

     '_o_r_i_g_i_n_a_l_S_u_p_p_o_r_t': '"logical"'; use for minimum support the
          original definition of the support of a rule (lhs and rhs)
          instead of lhs support.  Make sure to use 'ext = TRUE' if
          'originalSupport' is set to 'FALSE' (default: 'TRUE')

     Additional slots for Eclat in 'ECparameter':

     '_t_i_d_L_i_s_t': '"logical"'; return also a list of supporting
          transactions (transaction IDs) (default: 'FALSE')

_M_e_t_h_o_d_s:

     _c_o_e_r_c_e 'signature(from = "NULL", to = "APparameter")'

     _c_o_e_r_c_e 'signature(from = "list", to = "APparameter")'

     _c_o_e_r_c_e 'signature(from = "NULL", to = "ECparameter")'

     _c_o_e_r_c_e 'signature(from = "list", to = "ECparameter")'

     _s_h_o_w 'signature(object = "ASparameter")'

_A_u_t_h_o_r(_s):

     Bettina Gruen and Michael Hahsler

_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', 'eclat'

