measures               package:arules               R Documentation

_C_a_l_c_u_l_a_t_i_n_g _a_d_d_i_t_i_o_n_a_l _I_n_t_e_r_e_s_t _M_e_a_s_u_r_e_s _f_o_r _e_x_i_s_t_i_n_g _A_s_s_o_c_i_a_t_i_o_n_s

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

     Provides the generic functions and the needed S4 methods to
     calculate some additional interest measures for a set of  existing
     associations.

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

     all_confidence(x, ...)
     ## S4 method for signature 'itemsets':
     all_confidence(x, transactions = NULL, itemSupport = NULL)
     hyperlift(x, ...)
     ## S4 method for signature 'rules':
     hyperlift(x, transactions, d = 0.99)

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

       x: the set of associations. 

     ...: further arguments are passed on. 

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

itemSupport: alternatively to transactions, for some measures  a item
          support in the transaction data set is sufficient.

       d: the quantile used to calculate hyperlift. 

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

     Currently the interest measures all-confidence and hyperlift are
     implemented.

     All-confidence is defined on itemsets as the minimum confidence of
     all possible rule generated from the itemset.

     Hyperlift is an adaptation of the lift measure which is more
     robust  for low counts.

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

     A vector containing the values of the interest measure for each
     association in the set of associations 'x'.

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

     Michael Hahsler

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

     Edward R. Omiecinski. Alternative interest measures for mining 
     associations in databases. IEEE Transactions on Knowledge and 
     Data Engineering, 15(1):57-69, Jan/Feb 2003.

     Michael Hahsler, Kurt Hornik, and Thomas Reutterer.  Implications
     of probabilistic data modeling for rule mining.  Report 14,
     Research Report Series, Department of Statistics and  Mathematics,
     Wirschaftsuniversitt Wien, Augasse 2-6, 1090 Wien,  Austria,
     March 2005.

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

     'itemsets-class', 'rules-class'

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

     data("Income_transactions")

     ### calculate all-confidence
     itemsets <- apriori(Income_transactions, parameter = list(target = "freq")) 
     quality(itemsets) <- cbind(quality(itemsets), 
             all_confonfidence = all_confidence(itemsets))
     summary(itemsets)

     ### calculate hyperlift for the 0.9 quantile
     rules <- apriori(Income_transactions)
     quality(rules) <- cbind(quality(rules), 
             hyperlift = hyperlift(rules, Income_transactions, d = 0.9))
     inspect(SORT(rules, by = "hyperlift")[1:5])

