LkbConfidenceSupport          package:FKBL          R Documentation

_C_r_e_a_t_e_s _a _K_n_o_w_l_e_d_g_e _B_a_s_e

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

     This is the implementation of confidence and support method.
     Similar to LkbConfidenceSupportL. It is based in exploring every
     rule possibility, and test its confidence and its support. Support
     is a concept related with the amount of points covered by the
     rule. Confidence is related with the predominant class in the area
     covered by the rule. Only rules with any amount of support are
     included. The inferred class of a rule is set so the confidence is
     maximized.  There is a weight associated with a rule, and there
     are 4 different heuristic methods to obtain it, plus a fifth
     method which simply states an standard value of $\frac{1}{2} $.
     Described in chapter 2, pages 018-025 at Ishibuchi et al.$

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

      LkbConfidenceSupport(P,weight,train)

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

     Takes the vector of partitions, the weight of the method and the
     train dataset.

       P: The vector of partitions.

  weight: The weight of the method.

   train: The train dataset.

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

     Returns a knowledge base with the partitions and the rules.

_S_o_u_r_c_e:

     \begin{itemize}

     *  Ishibuchi, H., Nakashima, T., Nii, M.

     *  "Classification and modeling with linguistic information
        granules." 

     *  Soft Computing Approaches to Linguistic Data Mining. 

     *  Springer-Verlag, 2003 \end{itemize}

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

      data(P)
      data(trainA)
      LkbConfidenceSupport(P,0,trainA)

