discretize {arules} R Documentation

## 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`.

### 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.

### 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.

### Author(s)

Michael Hahsler

`cut`, `discretizeDF.supervised`.

### Examples

```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")
)