chiMerge: Discretization using the Chi-Merge method
In dprep: Data Pre-Processing and Visualization Functions for Classification

Description Usage Arguments Details Value Author(s) References See Also Examples

This function performs supervised discretization using the Chi Merge method.

1	chiMerge(data, varcon, alpha = 0.1,out=c("symb","num"))

`data`	The name of the dataset to be discretized
`varcon`	Vector of continuous variables
`alpha`	The significance level
`out`	To get the discretized data in numerical format enter "num". To get the discretized data in interval format enter "symb".

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 becomes slow when the number of variables increases (say for more than 100 variables).

discdata

A new data matrix containing the discretized features

Edgar Acuna, Jaime Porras, and Carlos Lopez

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

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

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

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found