discretize {arules}R Documentation

Convert a Continuous Variable into a Categorical Variable


This function implements several basic unsupervised methods to convert a continuous variable into a categorical variable (factor) using different binning strategies. For convenience, a whole data.frame can be discretized (i.e., all numeric columns are discretized).


  method = "frequency",
  breaks = 3,
  labels = NULL,
  include.lowest = TRUE,
  right = FALSE,
  dig.lab = 3,
  ordered_result = FALSE,
  infinity = FALSE,
  onlycuts = FALSE,
  categories = NULL,

discretizeDF(df, methods = NULL, default = NULL)



a numeric vector (continuous variable).


discretization method. Available are: "interval" (equal interval width), "frequency" (equal frequency), "cluster" (k-means clustering) and "fixed" (categories specifies interval boundaries). Note that equal frequency does not achieve perfect equally sized groups if the data contains duplicated values.

breaks, categories

either number of categories or a vector with boundaries for discretization (all values outside the boundaries will be set to NA). categories is deprecated, use breaks instead.


character vector; labels for the levels of the resulting category. By default, labels are constructed using "(a,b]" interval notation. If labels = FALSE, simple integer codes are returned instead of a factor..


logical; should the first interval be closed to the left?


logical; should the intervals be closed on the right (and open on the left) or vice versa?


integer; number of digits used to create labels.


logical; return a ordered factor?


logical; should the first/last break boundary changed to +/-Inf?


logical; return only computed interval boundaries?


for method "cluster" further arguments are passed on to kmeans.


data.frame; each numeric column in the data.frame is discretized.


named list of lists or a data.frame; the named list contains lists of discretization parameters (see parameters of discretize()) for each numeric column (see details). If no discretization is specified for a column, then the default settings for discretize() are used. Note: the names have to match exactly. If a data.frame is specified, then the discretization breaks in this data.frame are applied to df.


named list; parameters for discretize() used for all columns not specified in methods.


Discretize calculates breaks between intervals using various methods and then uses base::cut() to convert the numeric values into intervals represented as a factor.

Discretization may fail for several reasons. Some reasons are

discretize only implements unsupervised discretization. See arulesCBA::discretizeDF.supervised() in package arulesCBA for supervised discretization.

discretizeDF() applies discretization to each numeric column. Individual discretization parameters can be specified in the form: methods = list(column_name1 = list(method = ,...), column_name2 = list(...)). If no discretization method is specified for a column, then the discretization in default is applied (NULL invokes the default method in discretize()). The special method "none" can be specified to suppress discretization for a column.


discretize() returns a factor representing the categorized continuous variable with attribute "discretized:breaks" indicating the used breaks or and "discretized:method" giving the used method. If onlycuts = TRUE is used, a vector with the calculated interval boundaries is returned.

discretizeDF() returns a discretized data.frame.


Michael Hahsler

See Also

base::cut(), arulesCBA::discretizeDF.supervised().

Other preprocessing: hierarchy, itemCoding, merge(), sample()


x <- iris[,1]

### look at the distribution before discretizing
hist(x, breaks = 20, main = "Data")

def.par <- par(no.readonly = TRUE) # save default
layout(mat = rbind(1:2,3:4))

### convert continuous variables into categories (there are 3 types of flowers)
### the default method is equal frequency
table(discretize(x, breaks = 3))
hist(x, breaks = 20, main = "Equal Frequency")
abline(v = discretize(x, breaks = 3, 
  onlycuts = TRUE), col = "red")
# Note: the frequencies are not exactly equal because of ties in the data 

### equal interval width
table(discretize(x, method = "interval", breaks = 3))
hist(x, breaks = 20, main = "Equal Interval length")
abline(v = discretize(x, method = "interval", breaks = 3, 
  onlycuts = TRUE), col = "red")

### k-means clustering 
table(discretize(x, method = "cluster", breaks = 3))
hist(x, breaks = 20, main = "K-Means")
abline(v = discretize(x, method = "cluster", breaks = 3, 
  onlycuts = TRUE), col = "red")

### user-specified (with labels)
table(discretize(x, method = "fixed", breaks = c(-Inf, 6, Inf), 
    labels = c("small", "large")))
hist(x, breaks = 20, main = "Fixed")
abline(v = discretize(x, method = "fixed", breaks = c(-Inf, 6, Inf), 
    onlycuts = TRUE), col = "red")

par(def.par)  # reset to default

### prepare the iris data set for association rule mining
### use default discretization
irisDisc <- discretizeDF(iris)

### discretize all numeric columns differently
irisDisc <- discretizeDF(iris, default = list(method = "interval", breaks = 2, 
  labels = c("small", "large")))

### specify discretization for the petal columns and don't discretize the others
irisDisc <- discretizeDF(iris, methods = list(
  Petal.Length = list(method = "frequency", breaks = 3, 
    labels = c("short", "medium", "long")),
  Petal.Width = list(method = "frequency", breaks = 2, 
    labels = c("narrow", "wide"))
  default = list(method = "none")

### discretize new data using the same discretization scheme as the
###   data.frame supplied in methods. Note: NAs may occure if a new 
###   value falls outside the range of values observed in the 
###   originally discretized table (use argument infinity = TRUE in 
###   discretize to prevent this case.) 
discretizeDF(iris[sample(1:nrow(iris), 5),], methods = irisDisc)

[Package arules version 1.7-7 Index]