Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

APRIORI

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

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

Data Mining and R Programming for Beginners

v0.9.5
GPL-3
Authors
Alexandre Blansché [aut, cre]
Initial release

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.