APRIORI {fdm2id}

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.

See Also

predict.apriori, apriori-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)