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)
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