Overview
Association rule mining finds statements of the form when a basket contains A, it tends to also contain B. It is unsupervised: there is no response variable, and every item is a candidate on both sides.
tidy_apriori() wraps the Apriori algorithm from arules and returns
the rules as a tibble instead of an S4 object you have to
inspect() to read. The rest of the family filters, ranks
and applies those rules.
We use Groceries, a month of point-of-sale data from a
grocery outlet: 9,835 transactions over 169 item categories.
data("Groceries", package = "arules")
Groceries
#> transactions in sparse format with
#> 9835 transactions (rows) and
#> 169 items (columns)Mining Rules
Three numbers describe every rule, and two of them are thresholds you set up front:
- Support — the fraction of all transactions containing the whole rule. A support floor is what makes the search tractable, and what stops you acting on a pattern that occurred four times.
- Confidence — of the baskets containing the left-hand side, the fraction that also contain the right-hand side. This is the conditional probability.
- Lift — confidence divided by the right-hand side’s own frequency. Lift of 1 means the two are independent; above 1 means the left-hand side makes the right-hand side more likely than chance.
rules <- tidy_apriori(
Groceries,
support = 0.001, # at least ~10 of the 9,835 transactions
confidence = 0.5, # right-hand side follows at least half the time
minlen = 2 # rules with something on both sides
)
print(rules)
#> Tidy Apriori Results
#> ====================
#>
#> Parameters:
#> Minimum support: 0.001
#> Minimum confidence: 0.5
#> Rule length: 2 - 10
#>
#> Results:
#> Number of rules: 5668
#>
#> Quality Measure Summary:
#> Support: 0.0010 - 0.0223 (mean: 0.0017)
#> Confidence: 0.5000 - 1.0000 (mean: 0.6250)
#> Lift: 1.96 - 19.00 (mean: 3.26)
#>
#> Top 5 rules by lift:
#> # A tibble: 5 × 8
#> rule_id lhs rhs support confidence coverage lift count
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 53 {Instant food products,… {ham… 0.00122 0.632 0.00193 19.0 12
#> 2 37 {soda,popcorn} {sal… 0.00122 0.632 0.00193 16.7 12
#> 3 444 {flour,baking powder} {sug… 0.00102 0.556 0.00183 16.4 10
#> 4 327 {ham,processed cheese} {whi… 0.00193 0.633 0.00305 15.0 19
#> 5 55 {whole milk,Instant foo… {ham… 0.00153 0.5 0.00305 15.0 15
#>
#> Use inspect_rules() to view more rules
#> Use visualize_rules() to create visualizationsThe tibble is the part you work with:
rules$rules_tbl
#> # A tibble: 5,668 × 10
#> rule_id lhs rhs support confidence coverage lift count lhs_items
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <list>
#> 1 1 {honey} {who… 0.00112 0.733 0.00153 2.87 11 <chr [1]>
#> 2 2 {tidbits} {rol… 0.00122 0.522 0.00234 2.84 12 <chr [1]>
#> 3 3 {cocoa drink… {who… 0.00132 0.591 0.00224 2.31 13 <chr [1]>
#> 4 4 {pudding pow… {who… 0.00132 0.565 0.00234 2.21 13 <chr [1]>
#> 5 5 {cooking cho… {who… 0.00132 0.52 0.00254 2.04 13 <chr [1]>
#> 6 6 {cereals} {who… 0.00366 0.643 0.00569 2.52 36 <chr [1]>
#> 7 7 {jam} {who… 0.00295 0.547 0.00539 2.14 29 <chr [1]>
#> 8 8 {specialty c… {oth… 0.00427 0.5 0.00854 2.58 42 <chr [1]>
#> 9 9 {rice} {oth… 0.00397 0.52 0.00763 2.69 39 <chr [1]>
#> 10 10 {rice} {who… 0.00468 0.613 0.00763 2.40 46 <chr [1]>
#> # ℹ 5,658 more rows
#> # ℹ 1 more variable: rhs_items <list>
names(rules)
#> [1] "rules_tbl" "rules" "parameters" "n_rules"$rules holds the underlying arules object for anything
the tidy layer does not cover, in the same way $fit does
for tl_model().
Choosing the thresholds
Support and confidence trade recall against volume, and the trade is steep.
grid <- expand.grid(
support = c(0.001, 0.005, 0.01),
confidence = c(0.3, 0.5, 0.7)
)
grid$n_rules <- mapply(function(s, c) {
tidy_apriori(Groceries, support = s, confidence = c)$n_rules
}, grid$support, grid$confidence)
grid
#> support confidence n_rules
#> 1 0.001 0.3 13770
#> 2 0.005 0.3 482
#> 3 0.010 0.3 125
#> 4 0.001 0.5 5668
#> 5 0.005 0.5 120
#> 6 0.010 0.5 15
#> 7 0.001 0.7 1279
#> 8 0.005 0.7 1
#> 9 0.010 0.7 0Dropping the support floor by a factor of ten multiplies the rule count by more than a hundred. A high-confidence rule at very low support is usually a description of a handful of shoppers, not of the shop.
Reading the Rules
inspect_rules() sorts and takes the head, which is what
you want almost every time:
inspect_rules(rules, by = "lift", n = 10)
#> # A tibble: 10 × 10
#> rule_id lhs rhs support confidence coverage lift count lhs_items
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <list>
#> 1 53 {Instant foo… {ham… 0.00122 0.632 0.00193 19.0 12 <chr [2]>
#> 2 37 {soda,popcor… {sal… 0.00122 0.632 0.00193 16.7 12 <chr [2]>
#> 3 444 {flour,bakin… {sug… 0.00102 0.556 0.00183 16.4 10 <chr [2]>
#> 4 327 {ham,process… {whi… 0.00193 0.633 0.00305 15.0 19 <chr [2]>
#> 5 55 {whole milk,… {ham… 0.00153 0.5 0.00305 15.0 15 <chr [2]>
#> 6 4807 {other veget… {cre… 0.00102 0.588 0.00173 14.8 10 <chr [4]>
#> 7 330 {processed c… {whi… 0.00112 0.524 0.00214 12.4 11 <chr [2]>
#> 8 4858 {tropical fr… {but… 0.00102 0.667 0.00153 12.0 10 <chr [4]>
#> 9 2261 {hamburger m… {but… 0.00102 0.625 0.00163 11.3 10 <chr [3]>
#> 10 5638 {tropical fr… {but… 0.00102 0.625 0.00163 11.3 10 <chr [5]>
#> # ℹ 1 more variable: rhs_items <list>by also accepts "support",
"confidence" and "count". Set
decreasing = FALSE to look at the other end.
summarize_rules() gives the distribution of each quality
measure across the whole rule set:
summary_stats <- summarize_rules(rules)
summary_stats$n_rules
#> [1] 5668
data.frame(
measure = c("support", "confidence", "lift"),
min = c(summary_stats$support$min, summary_stats$confidence$min,
summary_stats$lift$min),
median = c(summary_stats$support$median, summary_stats$confidence$median,
summary_stats$lift$median),
max = c(summary_stats$support$max, summary_stats$confidence$max,
summary_stats$lift$max)
)
#> measure min median max
#> 1 support 0.001016777 0.00132181 0.02226741
#> 2 confidence 0.500000000 0.60000000 1.00000000
#> 3 lift 1.956824513 2.89899928 18.99565427Because the result is a tibble, dplyr works directly:
rules$rules_tbl |>
filter(lift > 5, count >= 15) |>
arrange(desc(confidence)) |>
select(lhs, rhs, confidence, lift, count)
#> # A tibble: 70 × 5
#> lhs rhs confidence lift count
#> <chr> <chr> <dbl> <dbl> <int>
#> 1 {liquor,red/blush wine} {bot… 0.905 11.2 19
#> 2 {tropical fruit,butter,curd} {yog… 0.789 5.66 15
#> 3 {tropical fruit,whole milk,rolls/buns,pastry} {yog… 0.789 5.66 15
#> 4 {tropical fruit,whole milk,soft cheese} {yog… 0.714 5.12 15
#> 5 {root vegetables,whipped/sour cream,cream chees… {yog… 0.714 5.12 15
#> 6 {tropical fruit,whipped/sour cream,margarine} {yog… 0.714 5.12 15
#> 7 {sausage,root vegetables,whipped/sour cream} {yog… 0.714 5.12 15
#> 8 {tropical fruit,whole milk,whipped/sour cream,r… {yog… 0.714 5.12 15
#> 9 {tropical fruit,root vegetables,whole milk,butt… {yog… 0.708 5.08 17
#> 10 {tropical fruit,whole milk,coffee} {yog… 0.704 5.04 19
#> # ℹ 60 more rowsWorking With One Item
filter_rules_by_item() narrows to rules mentioning an
item. where picks the side: "lhs" for what the
item leads to, "rhs" for what leads to it,
"both" for either.
# What predicts a purchase of whole milk?
filter_rules_by_item(rules, "whole milk", where = "rhs") |>
arrange(desc(lift)) |>
select(lhs, confidence, lift, count) |>
head(5)
#> # A tibble: 5 × 4
#> lhs confidence lift count
#> <chr> <dbl> <dbl> <int>
#> 1 {rice,sugar} 1 3.91 12
#> 2 {canned fish,hygiene articles} 1 3.91 11
#> 3 {root vegetables,butter,rice} 1 3.91 10
#> 4 {root vegetables,whipped/sour cream,flour} 1 3.91 17
#> 5 {butter,soft cheese,domestic eggs} 1 3.91 10
# And what does a basket containing yoghurt lead to?
filter_rules_by_item(rules, "yogurt", where = "lhs") |>
arrange(desc(lift)) |>
select(lhs, rhs, confidence, lift) |>
head(5)
#> # A tibble: 5 × 4
#> lhs rhs confidence lift
#> <chr> <chr> <dbl> <dbl>
#> 1 {other vegetables,curd,yogurt,whipped/sour cream} {cre… 0.588 14.8
#> 2 {tropical fruit,other vegetables,yogurt,white bread} {but… 0.667 12.0
#> 3 {hamburger meat,yogurt,whipped/sour cream} {but… 0.625 11.3
#> 4 {tropical fruit,other vegetables,whole milk,yogurt,dom… {but… 0.625 11.3
#> 5 {other vegetables,yogurt,whipped/sour cream,cream chee… {cur… 0.588 11.0find_related_items() is the shortcut for the common
question, with a lift floor built in so that co-occurrence by sheer
popularity is excluded:
find_related_items(rules, "yogurt", min_lift = 1.5, top_n = 5) |>
select(lhs, rhs, confidence, lift)
#> # A tibble: 5 × 4
#> lhs rhs confidence lift
#> <chr> <chr> <dbl> <dbl>
#> 1 {other vegetables,curd,yogurt,whipped/sour cream} {cre… 0.588 14.8
#> 2 {tropical fruit,other vegetables,yogurt,white bread} {but… 0.667 12.0
#> 3 {hamburger meat,yogurt,whipped/sour cream} {but… 0.625 11.3
#> 4 {tropical fruit,other vegetables,whole milk,yogurt,dom… {but… 0.625 11.3
#> 5 {other vegetables,yogurt,whipped/sour cream,cream chee… {cur… 0.588 11.0Recommending From a Basket
recommend_products() takes the items currently in a
basket and returns what the rules suggest adding.
recommend_products(
rules,
basket = c("flour", "baking powder"),
top_n = 5
)
#> # A tibble: 2 × 4
#> rhs confidence lift support
#> <chr> <dbl> <dbl> <dbl>
#> 1 {sugar} 0.556 16.4 0.00102
#> 2 {whole milk} 0.523 2.05 0.00925A rule only fires when the basket covers its entire left-hand side, so a basket of one or two very common items often matches nothing above the confidence floor:
recommend_products(rules, basket = c("whole milk", "butter"))
#> # A tibble: 0 × 4
#> # ℹ 4 variables: rhs <chr>, confidence <dbl>, lift <dbl>, support <dbl>That empty result is the honest answer rather than a failure.
whole milk appears in about a quarter of all baskets, so
very little follows it with 50% confidence.
min_confidence filters the rules you already mined, so
lowering the confidence floor below the one you mined at means mining
again. rules above was mined at
confidence = 0.5, and no amount of filtering will produce a
rule that was never generated:
broad <- tidy_apriori(
Groceries,
support = 0.001, confidence = 0.15, minlen = 2
)
broad$n_rules
#> [1] 26820
recommend_products(
broad,
basket = c("whole milk", "butter"),
min_confidence = 0.15,
top_n = 5
)
#> # A tibble: 5 × 4
#> rhs confidence lift support
#> <chr> <dbl> <dbl> <dbl>
#> 1 {domestic eggs} 0.218 3.43 0.00600
#> 2 {whipped/sour cream} 0.244 3.40 0.00671
#> 3 {curd} 0.177 3.32 0.00488
#> 4 {root vegetables} 0.299 2.74 0.00824
#> 5 {yogurt} 0.339 2.43 0.00935These confidences are weak on their own: none of the suggestions
follows in even half the baskets that hold milk and butter, which is why
rules found nothing. Lift is what makes them worth reading:
eggs, at the top, are more than three times as likely in such a basket
as in a basket picked at random. For common items, mine wide and rank on
lift.
Visualising
visualize_rules() returns a ggplot2 object for the
scatter method, so it composes like any other plot in the package.
visualize_rules(rules, method = "scatter", top_n = 200)
Support on one axis against confidence on the other, coloured by
lift, is the standard first look; top_n keeps the rules
with the highest lift. Rules sitting well away from the main cloud are
the ones to read.
visualize_rules(rules, method = "graph", top_n = 20)
The graph method needs arulesViz. It draws each rule as a circle, sized by support and coloured by lift, with arrows in from its left-hand items and out to its right-hand item, which is the more useful view once you have narrowed to a handful of rules worth reading.
From a Data Frame
Groceries is already a transactions object.
Real data usually arrives as one row per line item, which
arules coerces:
receipts <- data.frame(
basket_id = c(1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5),
item = c("bread", "butter", "jam",
"bread", "butter",
"bread", "butter", "jam",
"milk", "bread",
"bread", "butter", "jam"),
stringsAsFactors = TRUE
)
baskets <- split(as.character(receipts$item), receipts$basket_id)
transactions <- as(baskets, "transactions")
transactions
#> transactions in sparse format with
#> 5 transactions (rows) and
#> 4 items (columns)
small_rules <- tidy_apriori(
transactions,
support = 0.4, confidence = 0.6, minlen = 2
)
small_rules$rules_tbl |>
arrange(desc(lift)) |>
select(lhs, rhs, support, confidence, lift)
#> # A tibble: 9 × 5
#> lhs rhs support confidence lift
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 {jam} {butter} 0.6 1 1.25
#> 2 {butter} {jam} 0.6 0.75 1.25
#> 3 {bread,jam} {butter} 0.6 1 1.25
#> 4 {bread,butter} {jam} 0.6 0.75 1.25
#> 5 {jam} {bread} 0.6 1 1
#> 6 {bread} {jam} 0.6 0.6 1
#> 7 {butter} {bread} 0.8 1 1
#> 8 {bread} {butter} 0.8 0.8 1
#> 9 {butter,jam} {bread} 0.6 1 1With five baskets these numbers mean nothing — the point is the shape
of the input. Anything arules accepts as
transactions, tidy_apriori() accepts.
What the Numbers Do Not Tell You
Three cautions worth carrying:
- Lift is symmetric; the rule is not. A ⇒ B and B ⇒ A have the same lift and usually different confidence. Which direction you act on is a decision about the business, not a result from the data.
- A rule is not a cause. Bread and butter co-occur because people buy both, not because one drives the other. Moving butter next to bread is a testable hypothesis, and the test is an experiment.
- Rules describe the period you mined. A month of grocery data carries that month’s seasonality. Re-mine rather than reusing a stored rule set across a season boundary.
Function Reference
| Function | Purpose |
|---|---|
tidy_apriori() |
Mine rules, return a tibble |
tidy_rules() |
Convert an arules rules object to a tibble |
inspect_rules() |
Sort by a quality measure and take the head |
summarize_rules() |
Distribution of support, confidence and lift |
filter_rules_by_item() |
Rules mentioning an item, by side |
find_related_items() |
Items associated with one item, above a lift floor |
recommend_products() |
Suggestions for a given basket |
visualize_rules() |
Scatter, graph and grouped-matrix plots |