Introduction
Association rules are one of the most fundamental tools in data mining. An association rule has the form:
antecedent \(\Rightarrow\) consequent
where the antecedent (left-hand side) is a conjunction of predicates and the consequent (right-hand side) is a single predicate. The rule expresses that whenever the antecedent conditions are satisfied, the consequent tends to be satisfied as well.
For example:
middle_age & university_edu & IT_industry\(\Rightarrow\)high_income
This rule states that middle-aged people with a university education working in the IT industry tend to have a high income.
Association rules are evaluated using several quality measures:
- Support: the relative frequency of rows satisfying both the antecedent and the consequent. Higher support means the rule applies to more data.
- Confidence: the proportion of rows satisfying the antecedent that also satisfy the consequent. Higher confidence means the rule is more reliable.
- Coverage: the relative frequency of rows satisfying the antecedent.
- Lift: the ratio of the observed support to the expected support if the antecedent and consequent were independent. A lift greater than 1 indicates a positive association.
The nuggets package supports searching for association
rules in both crisp (Boolean) and
fuzzy data through the dig_associations()
function.
Before using the package, the required libraries must be loaded:
Data Preparation
For this tutorial, we use the built-in CO2 dataset,
which contains data from an experiment on the cold tolerance of the
grass species Echinochloa crus-galli. The dataset has 84
observations and includes information about the plant’s origin
(Type), treatment (Treatment), ambient CO2
concentration (conc), and CO2 uptake rate
(uptake).
head(CO2)
#> Plant Type Treatment conc uptake
#> 1 Qn1 Quebec nonchilled 95 16.0
#> 2 Qn1 Quebec nonchilled 175 30.4
#> 3 Qn1 Quebec nonchilled 250 34.8
#> 4 Qn1 Quebec nonchilled 350 37.2
#> 5 Qn1 Quebec nonchilled 500 35.3
#> 6 Qn1 Quebec nonchilled 675 39.2Before searching for association rules, data must be transformed into
predicates (logical or fuzzy columns). We use the
partition() function for this purpose. For a detailed
explanation of data preparation techniques, see the
vignette("data-preparation").
Crisp Data Preparation
We prepare a crisp version of the dataset by transforming factors into dummy variables and numeric columns into interval-based logical predicates:
crisp_co2 <- CO2 |>
select(-Plant) |>
partition(Type, Treatment) |>
partition(conc, .method = "crisp", .breaks = c(-Inf, 200, 500, Inf)) |>
partition(uptake, .method = "crisp", .breaks = c(-Inf, 20, 35, Inf))
head(crisp_co2, n = 3)
#> # A tibble: 3 × 10
#> `Type=Quebec` `Type=Mississippi` `Treatment=nonchilled` `Treatment=chilled`
#> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE FALSE TRUE FALSE
#> 2 TRUE FALSE TRUE FALSE
#> 3 TRUE FALSE TRUE FALSE
#> `conc=(-Inf;200]` `conc=(200;500]` `conc=(500;Inf]` `uptake=(-Inf;20]`
#> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE FALSE FALSE TRUE
#> 2 TRUE FALSE FALSE FALSE
#> 3 FALSE TRUE FALSE FALSE
#> `uptake=(20;35]` `uptake=(35;Inf]`
#> <lgl> <lgl>
#> 1 FALSE FALSE
#> 2 TRUE FALSE
#> 3 TRUE FALSEFuzzy Data Preparation
For a fuzzy version, we transform numeric columns into fuzzy predicates using triangular membership functions:
fuzzy_co2 <- CO2 |>
select(-Plant) |>
partition(Type, Treatment) |>
partition(conc, .method = "triangle", .breaks = c(-Inf, 95, 500, 1000, Inf)) |>
partition(uptake, .method = "triangle", .breaks = c(-Inf, 10, 25, 40, Inf))
head(fuzzy_co2, n = 3)
#> # A tibble: 3 × 10
#> `Type=Quebec` `Type=Mississippi` `Treatment=nonchilled` `Treatment=chilled`
#> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE FALSE TRUE FALSE
#> 2 TRUE FALSE TRUE FALSE
#> 3 TRUE FALSE TRUE FALSE
#> `conc=(-Inf;95;500)` `conc=(95;500;1000)` `conc=(500;1000;Inf)`
#> <dbl> <dbl> <dbl>
#> 1 1 0 0
#> 2 0.802 0.198 0
#> 3 0.617 0.383 0
#> `uptake=(-Inf;10;25)` `uptake=(10;25;40)` `uptake=(25;40;Inf)`
#> <dbl> <dbl> <dbl>
#> 1 0.6 0.4 0
#> 2 0 0.64 0.36
#> 3 0 0.347 0.653Basic Association Rule Search
The simplest use of dig_associations() searches for all
rules that meet given minimum support and confidence thresholds.
rules <- dig_associations(crisp_co2,
min_support = 0.1,
min_confidence = 0.8)
head(rules)
#> # A tibble: 6 × 13
#> antecedent consequent
#> <chr> <chr>
#> 1 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]}
#> 2 {Treatment=nonchilled,Type=Mississippi,conc=(200;500]} {uptake=(20;35]}
#> 3 {Treatment=chilled,Type=Mississippi} {uptake=(-Inf;20]}
#> 4 {Treatment=chilled,Type=Mississippi,conc=(200;500]} {uptake=(-Inf;20]}
#> 5 {Type=Mississippi,conc=(200;500],uptake=(-Inf;20]} {Treatment=chilled}
#> 6 {Type=Mississippi,conc=(200;500],uptake=(20;35]} {Treatment=nonchilled}
#> support confidence coverage conseq_support lift count antecedent_length pp
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 0.143 1 0.143 0.298 3.36 12 2 12
#> 2 0.107 1 0.107 0.345 2.90 9 3 9
#> 3 0.226 0.905 0.25 0.357 2.53 19 2 19
#> 4 0.107 1 0.107 0.357 2.8 9 3 9
#> 5 0.107 1 0.107 0.5 2 9 3 9
#> 6 0.107 1 0.107 0.5 2 9 3 9
#> pn np nn
#> <dbl> <dbl> <dbl>
#> 1 0 13 59
#> 2 0 20 55
#> 3 2 11 52
#> 4 0 21 54
#> 5 0 33 42
#> 6 0 33 42The result is a tibble containing found rules with their quality
measures: antecedent, consequent,
support, confidence, coverage,
consequent_support, lift, length,
and the contingency table columns (pp, pn,
np, nn).
For instance, the first rule represents the following association rule:
Type=Quebec & conc=(500;Inf] \(\Rightarrow\) uptake=(35;Inf]
Specifying Antecedent and Consequent
In many applications, you want to constrain which predicates can
appear on each side of the rule. This is done with the
antecedent and consequent arguments, which
accept tidyselect
expression expressions.
For example, to find rules that predict the uptake rate
from all other variables:
rules_uptake <- dig_associations(crisp_co2,
antecedent = !starts_with("uptake"),
consequent = starts_with("uptake"),
min_support = 0.1,
min_confidence = 0.8)
head(rules_uptake, n = 3)
#> # A tibble: 3 × 13
#> antecedent consequent
#> <chr> <chr>
#> 1 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]}
#> 2 {Treatment=nonchilled,Type=Mississippi,conc=(200;500]} {uptake=(20;35]}
#> 3 {Treatment=chilled,Type=Mississippi} {uptake=(-Inf;20]}
#> support confidence coverage conseq_support lift count antecedent_length pp
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 0.143 1 0.143 0.298 3.36 12 2 12
#> 2 0.107 1 0.107 0.345 2.90 9 3 9
#> 3 0.226 0.905 0.25 0.357 2.53 19 2 19
#> pn np nn
#> <dbl> <dbl> <dbl>
#> 1 0 13 59
#> 2 0 20 55
#> 3 2 11 52Using the Disjoint Argument
When data is prepared with partition(), a single
original variable is often expanded into multiple predicates (e.g.,
conc=(-Inf,200] and conc=(200,500]). These
predicates from the same variable should not appear together in the same
antecedent, as their conjunction would be contradictory or
redundant.
The disjoint argument prevents this. It accepts a
character vector of size equal to the number of columns in the input
data such that each unique value in the vector corresponds to a group of
mutually exclusive predicates.
By default, dig_associations() uses
var_names() on the column names to create the disjoint
vector automatically. This works well for data prepared with
partition().
You can also provide a custom disjoint vector:
disj <- var_names(colnames(crisp_co2))
disj
#> [1] "Type" "Type" "Treatment" "Treatment" "conc" "conc"
#> [7] "conc" "uptake" "uptake" "uptake"
rules <- dig_associations(crisp_co2,
disjoint = disj,
min_support = 0.1,
min_confidence = 0.8)
head(rules, n = 3)
#> # A tibble: 3 × 13
#> antecedent consequent
#> <chr> <chr>
#> 1 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]}
#> 2 {Treatment=nonchilled,Type=Mississippi,conc=(200;500]} {uptake=(20;35]}
#> 3 {Treatment=chilled,Type=Mississippi} {uptake=(-Inf;20]}
#> support confidence coverage conseq_support lift count antecedent_length pp
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 0.143 1 0.143 0.298 3.36 12 2 12
#> 2 0.107 1 0.107 0.345 2.90 9 3 9
#> 3 0.226 0.905 0.25 0.357 2.53 19 2 19
#> pn np nn
#> <dbl> <dbl> <dbl>
#> 1 0 13 59
#> 2 0 20 55
#> 3 2 11 52Controlling Rule Length
The min_length and max_length arguments
control the number of predicates in the antecedent:
# Find only rules with exactly 2 predicates in the antecedent
rules <- dig_associations(crisp_co2,
min_length = 2,
max_length = 2,
min_support = 0.1,
min_confidence = 0.8)
head(rules, n = 3)
#> # A tibble: 3 × 13
#> antecedent consequent support confidence
#> <chr> <chr> <dbl> <dbl>
#> 1 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]} 0.143 1
#> 2 {Treatment=chilled,Type=Mississippi} {uptake=(-Inf;20]} 0.226 0.905
#> 3 {Type=Mississippi,uptake=(20;35]} {Treatment=nonchilled} 0.179 0.882
#> coverage conseq_support lift count antecedent_length pp pn np nn
#> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 0.143 0.298 3.36 12 2 12 0 13 59
#> 2 0.25 0.357 2.53 19 2 19 2 11 52
#> 3 0.202 0.5 1.76 15 2 15 2 27 40Setting min_length = 0 generates rules with an empty
antecedent, which effectively computes the support of each consequent
alone.
Limiting the Number of Results
For large datasets, the number of possible rules can be enormous. The
max_results argument limits the total number of rules
generated:
rules <- dig_associations(crisp_co2,
min_support = 0.05,
min_confidence = 0.6,
max_results = 5)
nrow(rules)
#> [1] 5Fuzzy Association Rules
When the data contains fuzzy predicates (numeric columns with values
in [0, 1]), dig_associations() computes fuzzy support using
a t-norm for conjunction. The t_norm
argument specifies which t-norm to use:
-
"goguen"(default): the product t-norm - multiplies membership degrees -
"goedel": the minimum t-norm - takes the minimum of membership degrees -
"lukas": the Łukasiewicz t-norm = max(0, a + b - 1)
# Fuzzy rules using the product t-norm (default)
fuzzy_rules <- dig_associations(fuzzy_co2,
antecedent = !starts_with("uptake"),
consequent = starts_with("uptake"),
min_support = 0.05,
min_confidence = 0.6,
t_norm = "goguen")
head(fuzzy_rules, n = 3)
#> # A tibble: 3 × 13
#> antecedent consequent
#> <chr> <chr>
#> 1 {Type=Quebec} {uptake=(25;40;Inf)}
#> 2 {Treatment=nonchilled,Type=Quebec} {uptake=(25;40;Inf)}
#> 3 {Treatment=nonchilled,Type=Quebec,conc=(95;500;1000)} {uptake=(25;40;Inf)}
#> support confidence coverage conseq_support lift count antecedent_length pp
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 0.320 0.639 0.5 0.380 1.68 26.8 1 26.8
#> 2 0.177 0.709 0.25 0.380 1.87 14.9 2 14.9
#> 3 0.0894 0.876 0.102 0.380 2.31 7.51 3 7.51
#> pn np nn
#> <dbl> <dbl> <dbl>
#> 1 15.2 5.03 37.0
#> 2 6.11 17.0 46.0
#> 3 1.07 24.4 51.1The choice of t-norm affects how strictly the conjunction is evaluated. The Gödel t-norm is the least strict (produces higher support values), while the Łukasiewicz t-norm is the most strict. Note also that handling fuzzy data is generally much more slower and memory demanding than crisp data, especially for large datasets.
Computing Additional Interest Measures
The add_interest() function computes additional
interestingness measures for association rules beyond the basic support,
confidence, and lift. It uses the contingency table columns
(pp, pn, np, nn)
that are automatically included in the output of
dig_associations().
# Add selected interest measures
rules_enriched <- rules_uptake |>
add_interest(measures = c("conviction", "leverage", "jaccard"))
rules_enriched |>
select(antecedent, consequent, confidence, conviction, leverage, jaccard) |>
head(n = 3)
#> # A tibble: 3 × 6
#> antecedent consequent
#> <chr> <chr>
#> 1 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]}
#> 2 {Treatment=nonchilled,Type=Mississippi,conc=(200;500]} {uptake=(20;35]}
#> 3 {Treatment=chilled,Type=Mississippi} {uptake=(-Inf;20]}
#> confidence conviction leverage jaccard
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Inf 0.100 0.48
#> 2 1 Inf 0.0702 0.310
#> 3 0.905 6.75 0.137 0.594You can compute all available measures at once by omitting the
measures argument:
rules_all_measures <- rules_uptake |>
add_interest()
colnames(rules_all_measures)
#> [1] "antecedent" "consequent" "support"
#> [4] "confidence" "coverage" "conseq_support"
#> [7] "lift" "count" "antecedent_length"
#> [10] "pp" "pn" "np"
#> [13] "nn" "cosine" "conviction"
#> [16] "gini" "rule_power_factor" "odds_ratio"
#> [19] "relative_risk" "phi" "leverage"
#> [22] "collective_strength" "importance" "imbalance"
#> [25] "jaccard" "kappa" "lambda"
#> [28] "mutual_information" "maxconfidence" "j_measure"
#> [31] "kulczynski" "certainty" "added_value"
#> [34] "ralambondrainy" "sebag" "counterexample"
#> [37] "confirmed_confidence" "casual_support" "casual_confidence"
#> [40] "least_contradiction" "centered_confidence" "varying_liaison"
#> [43] "yule_q" "yule_y" "lerman"
#> [46] "implication_index" "doc" "fi"
#> [49] "dfi" "fe" "lci"
#> [52] "dlci" "lce" "uci"
#> [55] "duci" "uce"See the documentation of add_interest() for a complete
list of supported quality measures:
?add_interestSmoothing
Some interest measures may be undefined when contingency table counts
are zero (e.g., division by zero). The smooth_counts
argument applies Laplace smoothing to the counts before computing the
measures:
rules_smoothed <- rules_uptake |>
add_interest(measures = c("odds_ratio", "conviction"),
smooth_counts = 0.5)
rules_smoothed |>
select(antecedent, consequent, confidence, odds_ratio, conviction) |>
head(n = 3)
#> # A tibble: 3 × 5
#> antecedent consequent
#> <chr> <chr>
#> 1 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]}
#> 2 {Treatment=nonchilled,Type=Mississippi,conc=(200;500]} {uptake=(20;35]}
#> 3 {Treatment=chilled,Type=Mississippi} {uptake=(-Inf;20]}
#> confidence odds_ratio conviction
#> <dbl> <dbl> <dbl>
#> 1 1 110. 18.1
#> 2 1 51.4 13.0
#> 3 0.905 35.6 5.63GUHA Quantifiers
add_interest() also supports GUHA (General Unary
Hypothesis Automaton) quantifiers, including statistical tests based on
the binomial distribution:
rules_guha <- rules_uptake |>
add_interest(measures = c("dfi", "fe", "lci"),
p = 0.5)
rules_guha |>
select(antecedent, consequent, confidence, dfi, fe, lci) |>
head(n = 3)
#> # A tibble: 3 × 6
#> antecedent consequent
#> <chr> <chr>
#> 1 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]}
#> 2 {Treatment=nonchilled,Type=Mississippi,conc=(200;500]} {uptake=(20;35]}
#> 3 {Treatment=chilled,Type=Mississippi} {uptake=(-Inf;20]}
#> confidence dfi fe lci
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0.48 0.845 0.000244
#> 2 1 0.310 0.762 0.00195
#> 3 0.905 0.594 0.845 0.000111The p parameter represents the null-hypothesis
probability used in the binomial-test-based quantifiers
(lci, uci, dlci,
duci, lce, uce).
Excluding Redundant and Entailed Rules
In real-world datasets, some rules are trivially true or nearly so —
for example, rules that follow directly from the structure of the data
(e.g., engine_type=electric => fuel_type=electricity).
Such near-certain rules are called tautologies, and are
also referred to as implications (because they describe
an A => C relationship that almost always holds) or
axioms (because they can be assumed and used as
starting assumptions for pruning). In classical logic, a
tautology is strictly always true; here the term is used
loosely for rules whose confidence is very high in the data. This
distinction is a matter of logical philosophy and does not affect how
the functions work.
The excluded argument of dig_associations()
accepts a list of known implications (axioms). Each
axiom is a character vector where all elements except the last form the
antecedent and the last element is the consequent:
c(ant1, ant2, ..., antn, cons). The axioms are used to
prune generated rules via the modus ponens inference
rule:
- A rule’s consequent is excluded if it can be deduced from the rule’s antecedent using the axioms (possibly via a chain of axiom applications).
- A rule is pruned entirely if any predicate in the antecedent can be deduced from the remaining antecedent predicates via the axioms (it would be redundant).
Finding Near-Tautologies with dig_tautologies()
The dig_tautologies() function is specifically designed
to find rules with very high confidence (near-tautologies / axioms). It
searches iteratively, using rules found in earlier iterations to prune
the search space in later iterations, so that only the most concise
axioms are returned.
tautologies <- dig_tautologies(crisp_co2,
antecedent = everything(),
consequent = everything(),
min_confidence = 0.95,
min_support = 0.05,
max_length = 2)
tautologies
#> # A tibble: 7 × 13
#> antecedent consequent support confidence
#> <chr> <chr> <dbl> <dbl>
#> 1 {uptake=(35;Inf]} {Type=Quebec} 0.286 0.96
#> 2 {Type=Quebec,uptake=(-Inf;20]} {conc=(-Inf;200]} 0.0714 1
#> 3 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]} 0.143 1
#> 4 {Treatment=nonchilled,uptake=(-Inf;20]} {conc=(-Inf;200]} 0.0952 1
#> 5 {conc=(200;500],uptake=(-Inf;20]} {Type=Mississippi} 0.107 1
#> 6 {conc=(200;500],uptake=(-Inf;20]} {Treatment=chilled} 0.107 1
#> 7 {conc=(500;Inf],uptake=(20;35]} {Type=Mississippi} 0.0833 1
#> coverage conseq_support lift count antecedent_length pp pn np nn
#> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 0.298 0.5 1.92 24 1 24 1 18 41
#> 2 0.0714 0.286 3.5 6 2 6 0 18 60
#> 3 0.143 0.298 3.36 12 2 12 0 13 59
#> 4 0.0952 0.286 3.5 8 2 8 0 16 60
#> 5 0.107 0.5 2 9 2 9 0 33 42
#> 6 0.107 0.5 2 9 2 9 0 33 42
#> 7 0.0833 0.5 2 7 2 7 0 35 42Using Axioms to Filter Association Rules
Once axioms (near-tautologies) are identified, convert them to the
format expected by the excluded argument using
parse_condition(), passing both the antecedent and the
consequent columns, and pass them to
dig_associations():
# Convert tautologies to the excluded (axioms) format
excluded_conds <- parse_condition(tautologies$antecedent,
tautologies$consequent)
# Search for rules while excluding entailed patterns
rules_filtered <- dig_associations(crisp_co2,
antecedent = !starts_with("uptake"),
consequent = starts_with("uptake"),
excluded = excluded_conds,
min_support = 0.1,
min_confidence = 0.8)
rules_filtered
#> # A tibble: 4 × 13
#> antecedent consequent
#> <chr> <chr>
#> 1 {Treatment=nonchilled,Type=Mississippi,conc=(200;500]} {uptake=(20;35]}
#> 2 {Treatment=chilled,Type=Mississippi} {uptake=(-Inf;20]}
#> 3 {Treatment=chilled,Type=Mississippi,conc=(200;500]} {uptake=(-Inf;20]}
#> 4 {Type=Mississippi,conc=(-Inf;200]} {uptake=(-Inf;20]}
#> support confidence coverage conseq_support lift count antecedent_length pp
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 0.107 1 0.107 0.345 2.90 9 3 9
#> 2 0.226 0.905 0.25 0.357 2.53 19 2 19
#> 3 0.107 1 0.107 0.357 2.8 9 3 9
#> 4 0.131 0.917 0.143 0.357 2.57 11 2 11
#> pn np nn
#> <dbl> <dbl> <dbl>
#> 1 0 20 55
#> 2 2 11 52
#> 3 0 21 54
#> 4 1 19 53By providing known axioms, the search skips rules whose consequent can be deduced from their antecedent, and also prunes rules that contain redundant antecedent predicates (deducible from the remaining antecedent predicates). This focuses the results on genuinely interesting patterns and can run significantly faster on large datasets.
Manually Specifying Axioms
You can also construct the excluded list manually. Each
element is a character vector representing an implication (axiom): all
elements except the last form the antecedent and the last element is the
consequent:
# Axiom: "Treatment=chilled => Type=Mississippi"
# Any rule whose consequent is "Type=Mississippi" and whose antecedent contains
# "Treatment=chilled" will be excluded, because the consequent is deducible
# from the antecedent via this axiom.
manual_excluded <- list(c("Treatment=chilled", "Type=Mississippi"))
rules_manual <- dig_associations(crisp_co2,
antecedent = !starts_with("uptake"),
consequent = starts_with("uptake"),
excluded = manual_excluded,
min_support = 0.1,
min_confidence = 0.8)
rules_manual
#> # A tibble: 3 × 13
#> antecedent consequent
#> <chr> <chr>
#> 1 {Type=Quebec,conc=(500;Inf]} {uptake=(35;Inf]}
#> 2 {Treatment=nonchilled,Type=Mississippi,conc=(200;500]} {uptake=(20;35]}
#> 3 {Type=Mississippi,conc=(-Inf;200]} {uptake=(-Inf;20]}
#> support confidence coverage conseq_support lift count antecedent_length pp
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 0.143 1 0.143 0.298 3.36 12 2 12
#> 2 0.107 1 0.107 0.345 2.90 9 3 9
#> 3 0.131 0.917 0.143 0.357 2.57 11 2 11
#> pn np nn
#> <dbl> <dbl> <dbl>
#> 1 0 13 59
#> 2 0 20 55
#> 3 1 19 53This is useful when you have domain knowledge about which
implications are trivially true or undesirable, without needing to run
dig_tautologies() first.
Summary
This vignette demonstrated how to search for association rules using
the nuggets package:
Data preparation with
partition()transforms raw data into crisp or fuzzy predicates suitable for rule mining (seevignette("data-preparation")for details).Basic search with
dig_associations()finds rules meeting minimum support and confidence thresholds.Controlling the search space: use
antecedent/consequentto constrain which predicates appear on each side,disjointto prevent contradictory combinations viavar_names(), andmin_length/max_lengthto control rule complexity.Fuzzy rules extend the approach to graded membership using t-norms (
"goguen","goedel","lukas").Interest measures can be added with
add_interest()to evaluate rules from multiple perspectives (conviction, leverage, Jaccard, GUHA quantifiers, and many more).Excluding entailed rules with the
excludedargument anddig_tautologies()removes rules whose consequent is deducible from the antecedent via known axioms (near-tautologies / implications), and also prunes rules with redundant antecedent predicates (deducible from the remaining predicates). This speeds up the search and focuses results on genuinely interesting patterns.
For further details, consult the function documentation:
dig_associations(), add_interest(),
dig_tautologies(), parse_condition(),
partition(), var_names().
For more advanced usage with custom pattern types, see
vignette("custom-patterns").