APRIORI: Classification using APRIORI
In fdm2id: Data Mining and R Programming for Beginners

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,
  ...
)

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 paramters instead of a classification model.
`...`	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 July 9, 2023, 6:05 p.m.