Mine association rules using the Apriori algorithm with tidy output
Usage
tidy_apriori(
transactions,
support = 0.01,
confidence = 0.5,
minlen = 2,
maxlen = 10,
target = "rules"
)Arguments
- transactions
A transactions object or data frame
- support
Minimum support (default: 0.01)
- confidence
Minimum confidence (default: 0.5)
- minlen
Minimum rule length (default: 2)
- maxlen
Maximum rule length (default: 10)
- target
Type of association mined: "rules" (default), "frequent itemsets", "maximally frequent itemsets"
Value
A list of class "tidy_rules" containing:
rules_tbl: tibble of rules with lhs, rhs, and quality measures
rules: original rules object
parameters: parameters used
Examples
# \donttest{
if (requireNamespace("arules", quietly = TRUE)) {
data("Groceries", package = "arules")
# Basic apriori
rules <- tidy_apriori(Groceries, support = 0.001, confidence = 0.5)
# Access rules
rules$rules_tbl
}
#> Apriori
#>
#> Parameter specification:
#> confidence minval smax arem aval originalSupport maxtime support minlen
#> 0.5 0.1 1 none FALSE TRUE 5 0.001 2
#> maxlen target ext
#> 10 rules TRUE
#>
#> Algorithmic control:
#> filter tree heap memopt load sort verbose
#> 0.1 TRUE TRUE FALSE TRUE 2 TRUE
#>
#> Absolute minimum support count: 9
#>
#> set item appearances ...[0 item(s)] done [0.00s].
#> set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
#> sorting and recoding items ... [157 item(s)] done [0.00s].
#> creating transaction tree ... done [0.00s].
#> checking subsets of size 1 2 3 4 5 6 done [0.01s].
#> writing ... [5668 rule(s)] done [0.00s].
#> creating S4 object ... done [0.00s].
#> # A tibble: 5,668 × 8
#> rule_id lhs rhs support confidence coverage lift count
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 {honey} {whole m… 0.00112 0.733 0.00153 2.87 11
#> 2 2 {tidbits} {rolls/b… 0.00122 0.522 0.00234 2.84 12
#> 3 3 {cocoa drinks} {whole m… 0.00132 0.591 0.00224 2.31 13
#> 4 4 {pudding powder} {whole m… 0.00132 0.565 0.00234 2.21 13
#> 5 5 {cooking chocolate} {whole m… 0.00132 0.52 0.00254 2.04 13
#> 6 6 {cereals} {whole m… 0.00366 0.643 0.00569 2.52 36
#> 7 7 {jam} {whole m… 0.00295 0.547 0.00539 2.14 29
#> 8 8 {specialty cheese} {other v… 0.00427 0.5 0.00854 2.58 42
#> 9 9 {rice} {other v… 0.00397 0.52 0.00763 2.69 39
#> 10 10 {rice} {whole m… 0.00468 0.613 0.00763 2.40 46
#> # ℹ 5,658 more rows
# }