Supervised Methods to Convert Continuous Variables into Categorical Variables
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).
discretizeDF.supervised(formula, data, method = "mdlp", dig.lab = 3, ...)
formula |
a formula object to specify the class variable for supervised discretization and the
predictors to be discretized in the form |
data |
a data.frame containing continuous variables to be discretized |
method |
discretization method. Available are:
|
dig.lab |
integer; number of digits used to create labels. |
... |
Additional parameters are passed on to the implementation of the chosen discretization method. |
discretizeDF.supervised only implements supervised discretization. See
discretizeDF in package arules for unsupervised discretization.
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.
Michael Hahsler
Unsupervised discretization from arules:
discretize,
discretizeDF.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.