Classification using APRIORI
This function builds a classification model using the association rules method APRIORI.
APRIORI( train, labels, supp = 0.05, conf = 0.8, prune = FALSE, tune = FALSE, ... )
train |
The training set (description), as a |
labels |
Class labels of the training set ( |
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: |
tune |
If true, the function returns paramters instead of a classification model. |
... |
Other parameters. |
The classification model, as an object of class apriori.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.