pat_apriori: Apriori rules

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Apriori rules

Description

Frequent itemsets and association rules using arules::apriori.

Usage

pat_apriori(
  target = c("rules", "frequent itemsets"),
  supp = 0.5,
  conf = 0.9,
  minlen = 2,
  maxlen = 10,
  lhs = NULL,
  rhs = NULL,
  include = NULL,
  exclude = NULL,
  quality_filter = NULL,
  control = NULL
)

Arguments

target	mining target: "rules" or "frequent itemsets"
supp	minimum support threshold
conf	minimum confidence threshold for rules
minlen	minimum pattern length
maxlen	maximum pattern length
lhs	optional vector of items constrained to the left-hand side of rules
rhs	optional vector of items constrained to the right-hand side of rules
include	optional vector of items allowed in the discovered patterns
exclude	optional vector of items forbidden in the discovered patterns
quality_filter	optional quality filter created with patutils()
control	list of control parameters

Value

returns a pat_apriori object

Examples

if (requireNamespace("arules", quietly = TRUE)) {
 data("AdultUCI", package = "arules")
 trans <- suppressWarnings(methods::as(as.data.frame(AdultUCI), "transactions"))
 utils <- patutils()
 pm <- pat_apriori(
   target = "rules",
   supp = 0.2,
   conf = 0.85,
   minlen = 2,
   maxlen = 3,
   rhs = c("native-country=United-States"),
   quality_filter = utils$quality_min(confidence = 0.9, lift = 1.03),
   control = list(verbose = FALSE)
 )
 pm <- fit(pm, trans)
 rules <- suppressWarnings(discover(pm, trans))
 eval <- evaluate(pm, rules)
 eval$metrics
}

daltoolbox documentation built on May 14, 2026, 9:06 a.m.