arules (version 1.7-1)

discretize: Convert a Continuous Variable into a Categorical Variable

Description

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).

Usage

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

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

Arguments

x

a numeric vector (continuous variable).

method

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

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

labels

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..

include.lowest

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

right

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

dig.lab

integer; number of digits used to create labels.

ordered_result

logical; return a ordered factor?

infinity

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

onlycuts

logical; return only computed interval boundaries?

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

df

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

methods

named list of lists or a data.frame; the named list contains list of discretization parameters (see parameters of discretize) for each numeric column (see details). If no specific 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.

default

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

Value

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.

Details

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

Discretization may fail for several reasons. Some reasons are

• A variable contains only a single value. In this case, the variable should be dropped or directly converted into a factor with a single level (see factor).

• Some calculated breaks are not unique. This can happen for method frequency with very skewed data (e.g., a large portion of the values is 0). In this case, non-unique breaks are dropped with a warning. It would be probably better to look at the histogram of the data and decide on breaks for the method fixed.

discretize only implements unsupervised discretization. See 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.

Examples

# NOT RUN {
data(iris)
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")
)