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Association rule mining can produce more rules than are practical to inspect. An effective workflow constrains the search, filters and ranks the result, and then removes rules that add no information.
trans <- transactions(list(
T1 = c("bread", "butter", "milk"),
T2 = c("bread", "butter"),
T3 = c("bread", "milk"),
T4 = c("bread", "butter", "jam"),
T5 = c("bread", "butter", "milk"),
T6 = c("butter", "jam"),
T7 = c("bread", "milk", "cereal"),
T8 = c("bread", "butter", "jam")
))Support, confidence, and rule length constrain the rule set while
Apriori is searching. The appearance argument can also
restrict items to the left- or right-hand side. Here, Apriori generates
only rules that predict butter or milk.
rules <- apriori(
trans,
parameter = list(
support = 0.25, confidence = 0.6,
maxlen = 3
),
appearance = list(
rhs = c("butter", "milk"),
default = "lhs"
)
)
#> Apriori
#>
#> Parameter specification:
#> confidence minval smax arem aval originalSupport maxtime support minlen
#> 0.6 0.1 1 none FALSE TRUE 5 0.25 1
#> maxlen target ext
#> 3 rules TRUE
#>
#> Algorithmic control:
#> filter tree heap memopt load sort verbose
#> 0.1 TRUE TRUE FALSE TRUE 2 TRUE
#>
#> Absolute minimum support count: 2
#>
#> set item appearances ...[2 item(s)] done [0.00s].
#> set transactions ...[5 item(s), 8 transaction(s)] done [0.00s].
#> sorting and recoding items ... [4 item(s)] done [0.00s].
#> creating transaction tree ... done [0.00s].
#> checking subsets of size 1 2 3 done [0.00s].
#> writing ... [4 rule(s)] done [0.00s].
#> creating S4 object ... done [0.00s].
inspect(rules)
#> lhs rhs support confidence coverage lift count
#> [1] {} => {butter} 0.750 0.7500000 1.000 1.000000 6
#> [2] {jam} => {butter} 0.375 1.0000000 0.375 1.333333 3
#> [3] {bread} => {butter} 0.625 0.7142857 0.875 0.952381 5
#> [4] {bread, jam} => {butter} 0.250 1.0000000 0.250 1.333333 2These constraints produce only 4 rules. Constraining the search also reduces its memory and computation requirements.
Filter by criteria appropriate for the task, then rank the remaining rules. Keeping these criteria in the code makes the selection reproducible.
selected <- subset(rules, lift > 1 & confidence >= 0.7)
ranked <- sort(selected, by = "lift", decreasing = TRUE)
inspect(ranked)
#> lhs rhs support confidence coverage lift count
#> [1] {jam} => {butter} 0.375 1 0.375 1.333333 3
#> [2] {bread, jam} => {butter} 0.250 1 0.250 1.333333 2Many interest measures are available in addition to support,
confidence, and lift. The vignette Interest measures
(vignette("interest-measures", package = "arules"))
introduces the use of additional interest measures.
A rule is redundant if a more general rule with the same consequent performs at least as well according to the selected measure. Removing redundant rules produces a more concise result.
non_redundant <- rules[!is.redundant(rules)]
inspect(sort(non_redundant, by = "lift"))
#> lhs rhs support confidence coverage lift count
#> [1] {jam} => {butter} 0.375 1.00 0.375 1.333333 3
#> [2] {} => {butter} 0.750 0.75 1.000 1.000000 6The complementary subset contains the redundant rules that were removed.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.