Weka_associators            package:RWeka            R Documentation

_R/_W_e_k_a _A_s_s_o_c_i_a_t_o_r_s

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

     R interfaces to Weka association rule learning algorithms.

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

     Apriori(x, control = NULL)
     Tertius(x, control = NULL)

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

       x: an R object with the data to be associated.

 control: an object of class 'Weka_control', or a character vector of
          control options, or 'NULL' (default). Available options can
          be obtained on-line using the Weka Option Wizard 'WOW', or
          the Weka documentation.

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

     'Apriori' implements an Apriori-type algorithm, which iteratively
     reduces the minimum support until it finds the required number of
     rules with the given minimum confidence.

     'Tertius' implements a Tertius-type algorithm.

     See the references for more information on these algorithms.

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

     A list inheriting from class 'Weka_associators' with components
     including 

associator: a reference (of class 'jobjRef') to a Java object obtained
          by applying the Weka 'buildAssociations' method to the
          training instances using the given control options.

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

     R. Agrawal and R. Srikant (1994). Fast algorithms for mining
     association rules in large databases. _Proceedings of the 
     International Conference on Very Large Databases_, 478-499.
     Santiago, Chile: Morgan Kaufmann, Los Altos, CA.

     P. A. Flach and N. Lachiche (1999). Confirmation-guided discovery
     of first-order rules with Tertius. _Machine Learning_, *42*,
     61-95.

     I. H. Witten and E. Frank (2005). _Data Mining: Practical Machine
     Learning Tools and Techniques_. 2nd Edition, Morgan Kaufmann, San
     Francisco.

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

     x <- read.arff(system.file("arff", "contact-lenses.arff",
                                package = "RWeka"))
     ## Apriori with defaults.
     Apriori(x)
     ## Some options: set required number of rules to 20.
     Apriori(x, Weka_control(N = 20))

     ## Tertius with defaults.
     Tertius(x)
     ## Some options: only classification rules (single item in the RHS).
     Tertius(x, Weka_control(S = TRUE))

