RWeka (version 0.4-46)

Weka_filters: R/Weka Filters

Description

R interfaces to Weka filters.

Usage

Normalize(formula, data, subset, na.action, control = NULL)
Discretize(formula, data, subset, na.action, control = NULL)

Value

A data frame.

Arguments

formula

a symbolic description of a model. Note that for unsupervised filters the response can be omitted.

data

an optional data frame containing the variables in the model.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

na.action

a function which indicates what should happen when the data contain NAs. See model.frame for details.

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.

Details

Normalize implements an unsupervised filter that normalizes all instances of a dataset to have a given norm. Only numeric values are considered, and the class attribute is ignored.

Discretize implements a supervised instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by Fayyad & Irani's MDL method (the default).

Note that these methods ignore nominal attributes, i.e., variables of class factor.

References

U. M. Fayyad and K. B. Irani (1993). Multi-interval discretization of continuous-valued attributes for classification learning. Thirteenth International Joint Conference on Artificial Intelligence, 1022--1027. Morgan Kaufmann.

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

Examples

## Using a Weka data set ...
w <- read.arff(system.file("arff","weather.arff",
	       package = "RWeka"))

## Normalize (response irrelevant)
m1 <- Normalize(~., data = w)
m1

## Discretize
m2 <- Discretize(play ~., data = w)
m2