ruleInduction             package:arules             R Documentation

_R_u_l_e _i_n_d_u_c_t_i_o_n _f_o_r _a _s_e_t _o_f _i_t_e_m_s_e_t_s

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

     Provides the generic function and the needed S4 method to induce
     all rules which can be generated by the given itemsets from a
     transactions data set.

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

     ruleInduction(x, ...)
     ## S4 method for signature 'itemsets':
     ruleInduction(x, transactions, verbose = FALSE)

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

       x: the set of itemsets from which rules will be induced. 

     ...: further arguments are passed on. 

transactions: the transaction data set used to mine  the itemsets. 

 verbose: logical; report progress. 

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

     Currently, only a very simple rule induction method is
     implemented. All rules are mined from the transactions data set
     using the minimal support found in itemsets. And in a second step
     all rules which do not stem from one of the itemsets are removed.
     This procedure will be in many cases very slow (e.g., for itemsets
      with many elements or very low support).

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

     An object of class 'rules'.

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

     Michael Hahsler

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

     'itemsets-class', 'rules-class' 'transactions-class'

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

     data("Income_transactions")

     ### find all closed frequent itemsets
     closed <- apriori(Income_transactions, 
             parameter = list(target = "closed", support = 0.4))

     ### rule induction
     rules <- ruleInduction(closed, Income_transactions, verbose = TRUE)

     ### inspect the resulting rules
     inspect(SORT(rules, by = "lift")[1:5])

