---
title: "Association Rules"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Association Rules}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r, include=FALSE}
options(tibble.width = Inf)
```



# 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:

```{r, message=FALSE}
library(nuggets)
library(dplyr)    # for data manipulation
```



# 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`).

```{r}
head(CO2)
```

Before 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:

```{r}
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)
```

## Fuzzy Data Preparation

For a fuzzy version, we transform numeric columns into fuzzy predicates
using triangular membership functions:

```{r}
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)
```


# Basic Association Rule Search

The simplest use of `dig_associations()` searches for all rules that
meet given minimum support and confidence thresholds.

```{r}
rules <- dig_associations(crisp_co2,
                          min_support = 0.1,
                          min_confidence = 0.8)
head(rules)
```

The 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:

```{r, include=FALSE}
to_logical <- function(x) {
    x <- gsub("{", "", x, fixed = TRUE)
    x <- gsub("}", "", x, fixed = TRUE)
    x <- gsub(",", " & ", x, fixed = TRUE)
}
```

> `r paste0("> ", to_logical(rules$antecedent[1]), " $\\Rightarrow$ ", to_logical(rules$consequent[1]))`



# 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](https://tidyselect.r-lib.org/reference/language.html) expressions.

For example, to find rules that predict the `uptake` rate from all other
variables:

```{r}
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)
```


# Using 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:

```{r}
disj <- var_names(colnames(crisp_co2))
disj

rules <- dig_associations(crisp_co2,
                          disjoint = disj,
                          min_support = 0.1,
                          min_confidence = 0.8)
head(rules, n = 3)
```


# Controlling Rule Length

The `min_length` and `max_length` arguments control the number of predicates
in the antecedent:

```{r}
# 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)
```

Setting `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:

```{r}
rules <- dig_associations(crisp_co2,
                          min_support = 0.05,
                          min_confidence = 0.6,
                          max_results = 5)
nrow(rules)
```


# Fuzzy 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)

```{r}
# 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)
```

The 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()`.

```{r}
# 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)
```

You can compute all available measures at once by omitting the `measures`
argument:

```{r}
rules_all_measures <- rules_uptake |>
    add_interest()
colnames(rules_all_measures)
```

See the documentation of `add_interest()` for a complete list of supported
quality measures:

```{r, eval=FALSE}
?add_interest
```



## Smoothing

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:

```{r}
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)
```

## GUHA Quantifiers

`add_interest()` also supports GUHA (General Unary Hypothesis Automaton)
quantifiers, including statistical tests based on the binomial distribution:

```{r}
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)
```

The `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.

```{r}
tautologies <- dig_tautologies(crisp_co2,
                               antecedent = everything(),
                               consequent = everything(),
                               min_confidence = 0.95,
                               min_support = 0.05,
                               max_length = 2)
tautologies
```


## Using 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()`:

```{r}
# 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
```

By 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:

```{r}
# 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
```

This 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:

1. **Data preparation** with `partition()` transforms raw data into crisp
   or fuzzy predicates suitable for rule mining (see `vignette("data-preparation")`
   for details).

2. **Basic search** with `dig_associations()` finds rules meeting minimum
   support and confidence thresholds.

3. **Controlling the search space**: use `antecedent`/`consequent` to
   constrain which predicates appear on each side, `disjoint` to prevent
   contradictory combinations via `var_names()`, and `min_length`/`max_length`
   to control rule complexity.

4. **Fuzzy rules** extend the approach to graded membership using t-norms
   (`"goguen"`, `"goedel"`, `"lukas"`).

5. **Interest measures** can be added with `add_interest()` to evaluate
   rules from multiple perspectives (conviction, leverage, Jaccard, GUHA
   quantifiers, and many more).

6. **Excluding entailed rules** with the `excluded` argument and
   `dig_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")`.
