chiMerge                package:dprep                R Documentation

_D_i_s_c_r_e_t_i_z_a_t_i_o_n _u_s_i_n_g _t_h_e _C_h_i-_M_e_r_g_e _m_e_t_h_o_d

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

     This function performs supervised discretization using the Chi
     Merge method

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

     chiMerge(data, varcon, alpha = 0.1)

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

    data: The name of the dataset to be discretize

  varcon: Vector of continuous variables

   alpha: The significance level

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

     In case of datasets containing negative values  apply first a
     range normalization to change the range of the attributes values
     to an interval containing positive values. The discretization
     process slows down when the number of variables increases (say for
     more  than 100 variables).

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

discdata : A new data matrix containing the discretized features

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

     Edgar Acuna, Jaime Porras, and  Carlos Lopez

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

     Kantardzic M. (2003). Data Mining: Concepts, Models, methods, and
     Algorithms.  John Wiley. New York.

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

     'disc.ef', 'disc.ew','disc.1r','disc.mentr'

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

     #-----Discretization using the ChiMerge method
     data(my.iris)
     a=chiMerge(my.iris,1:4,alpha=0.05)
     #-----Applying chiMerge a dataset containing negative values 
     #data(ionosphere)
     #normionos=rangenorm(ionosphere,"mmnorm")
     #b=chiMerge(normionos,1:32)

