fdm2id (version 1.0.1)

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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Value

The classification model, as an object of class apriori.

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.

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)