chiMerge {dprep} | R Documentation

## Discretization using the Chi-Merge method

### Description

This function performs supervised discretization using the Chi Merge method

### Usage

    chiMerge(data, varcon, alpha = 0.1)

### Arguments

`data` | The name of the dataset to be discretize
`varcon` | Vector of continuous variables
`alpha` | The significance level

### Details

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).

### Value

`discdata` | A new data matrix containing the discretized features

### Author(s)

Edgar Acuna, Jaime Porras, and Carlos Lopez

### References

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

### See Also

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

### Examples

    #-----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)

[Package dprep version 1.0 Index]