APRIORI: Classification using APRIORI
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APRIORI

R Documentation

Classification using APRIORI

Description

This function builds a classification model using the association rules method APRIORI.

Usage

APRIORI(
  train,
  labels,
  supp = 0.05,
  conf = 0.8,
  prune = FALSE,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`supp`	The minimal support of an item set (numeric value).
`conf`	The minimal confidence of an item set (numeric value).
`prune`	A logical indicating whether to prune redundant rules or not (default: `FALSE`).
`tune`	If true, the function returns parameters instead of a classification model.
`methodparameters`	Present for interface consistency with `performance` (which always passes it when fitting a model). Currently unused: `APRIORI` does not support reusing pre-tuned parameters.
`graph`	Present for interface consistency with `performance` (which always passes it when fitting a model). Currently unused: `APRIORI` does not produce a plot.
`seed`	A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.
`...`	Other parameters.

Value

The classification model, as an object of class apriori.

Examples

require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)

fdm2id documentation built on Sept. 4, 2026, 5:07 p.m.