discretizeDF.supervised: Supervised Methods to Convert Continuous Variables into...

In ianjjohnson/arulesCBA: Classification Based on Association Rules

Supervised Methods to Convert Continuous Variables into Categorical Variables

Description

This function implements several supervised methods to convert continuous variables into a categorical variables (factor) suitable for association rule mining and building associative classifiers. A whole data.frame is discretized (i.e., all numeric columns are discretized).

Usage

discretizeDF.supervised(formula, data, method = "mdlp", dig.lab = 3, ...)

Arguments

formula	a formula object to specify the class variable for supervised discretization and the predictors to be discretized in the form class ~ . or class ~ predictor1 + predictor2.
data	a data.frame containing continuous variables to be discretized
method	discretization method. Available are: "mdlp", "caim", "cacc", "ameva", "chi2", "chimerge", "extendedchi2", and "modchi2".
dig.lab	integer; number of digits used to create labels.
...	Additional parameters are passed on to the implementation of the chosen discretization method.

Details

discretizeDF.supervised only implements supervised discretization. See discretizeDF in package arules for unsupervised discretization.

Value

discretizeDF returns a discretized data.frame. Discretized columns have an attribute "discretized:breaks" indicating the used breaks or and "discretized:method" giving the used method.

Author(s)

Michael Hahsler

See Also

Unsupervised discretization from arules: discretize, discretizeDF.

Details about the available supervised discretization methods from discretization: mdlp, caim, cacc, ameva, chi2, chiM, extendChi2, modChi2.

Examples

data("iris")
summary(iris)

# supervised discretization using Species
iris.disc <- discretizeDF.supervised(Species ~ ., iris)
summary(iris.disc)

attributes(iris.disc$Sepal.Length)

# discretize the first few instances of iris using the same breaks as iris.disc
discretizeDF(head(iris), methods = iris.disc)

# only discretize predictors Sepal.Length and Petal.Length
iris.disc2 <- discretizeDF.supervised(Species ~ Sepal.Length + Petal.Length, iris)
head(iris.disc2)

ianjjohnson/arulesCBA documentation built on June 13, 2022, 2:07 p.m.