apriori                package:arules                R Documentation

_M_i_n_i_n_g _A_s_s_o_c_i_a_t_i_o_n_s _w_i_t_h _A_p_r_i_o_r_i

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

     Mine frequent itemsets, association rules or association
     hyperedges using the Apriori algorithm.  The Apriori algorithm
     employs level-wise search for frequent itemsets.  The
     implementation of Apriori used includes some improvements (e.g., a
     prefix tree and item sorting).

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

     apriori(data, parameter = NULL, appearance = NULL, control = NULL)

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

    data: object of class 'transactions' or any data structure which
          can be coerced into 'transactions' (e.g., a binary matrix or
          data.frame).

parameter: object of class 'APparameter' or named list. The default
          behavior is to mine rules with support 0.1, confidence 0.8,
          and maxlen 5.

appearance: object of class 'APappearance' or named list. With this
          argument item appearance can be restricted. By default all
          items can appear unrestricted.

 control: object of class 'APcontrol' or named list. Controls the
          performance of the mining algorithm (item sorting, etc.)

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

     Calls the C implementation of the Apriori algorithm by Christian
     Borgelt for mining frequent itemsets, rules or hyperedges.

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

     Returns an object of class 'rules' or 'itemsets'.

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

     R. Agrawal, T. Imielinski, and A. Swami (1993) Mining association
     rules between sets of items in large databases. In _Proceedings of
     the ACM SIGMOD International Conference on Management of Data_,
     pages 207-216, Washington D.C.

     Christian Borgelt and Rudolf Kruse (2002) Induction of Association
     Rules: Apriori Implementation. _15th Conference on Computational
     Statistics_ (COMPSTAT 2002, Berlin, Germany) Physica Verlag,
     Heidelberg, Germany.

     Christian Borgelt (2003) Efficient Implementations of Apriori and
     Eclat.  _Workshop of Frequent Item Set Mining Implementations_
     (FIMI 2003, Melbourne, FL, USA).

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

     'APparameter-class', 'APcontrol-class', 'APappearance-class',
     'transactions-class', 'itemsets-class', 'rules-class'

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

     data("Adult")
     ## Mine association rules.
     rules <- apriori(Adult, 
                      parameter = list(supp = 0.5, conf = 0.9,
                                       target = "rules"))
     summary(rules)

