daltoolbox (version 1.3.787)

pat_apriori: Apriori rules

Description

Frequent itemsets and association rules using arules::apriori.

Usage

pat_apriori(
  target = c("rules", "frequent itemsets"),
  supp = 0,
  conf = 0,
  support_strategy = pat_support_threshold("curvature"),
  confidence_strategy = pat_confidence_threshold(),
  minlen = 2,
  maxlen = 10,
  lhs = NULL,
  rhs = NULL,
  include = NULL,
  exclude = NULL,
  quality_filter = NULL,
  rule_filter = pat_rule_filter_none(),
  control = NULL
)

Value

returns a pat_apriori object

Arguments

target

mining target: "rules" or "frequent itemsets"

supp

minimum support threshold. If 0, estimated during fit() using support_strategy.

conf

minimum confidence threshold for rules. If 0, estimated during fit() using confidence_strategy.

support_strategy

support threshold strategy created with pat_support_threshold()

confidence_strategy

confidence threshold strategy created with pat_confidence_threshold()

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()

rule_filter

rule filter created with pat_rule_filter_none(), pat_rule_filter_interest(), or pat_rule_filter_dara()

control

list of control parameters

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,
   conf = 0,
   support_strategy = pat_support_threshold("curvature"),
   confidence_strategy = pat_confidence_threshold("rhs_baseline", margin = 0.1),
   minlen = 2,
   maxlen = 3,
   rhs = c("native-country=United-States"),
   quality_filter = utils$quality_min(confidence = 0.9, lift = 1.03),
   rule_filter = pat_rule_filter_interest(lift_min = 1),
   control = list(verbose = FALSE)
 )
 pm <- fit(pm, trans)
 rules <- suppressWarnings(discover(pm, trans))
 eval <- evaluate(pm, rules)
 eval$metrics
}