dig_implications: Search for association rules

In nuggets: Extensible Data Pattern Searching Framework

Search for association rules

Description

Association rules identify conditions (antecedents) under which a specific feature (consequent) is present very often.

Scheme:

⁠A => C⁠

If condition A is satisfied, then the feature C is present very often.

Example:

⁠university_edu & middle_age & IT_industry => high_income⁠

People in middle age with university education working in IT industry have very likely a high income.

Antecedent A is usually a set of predicates, and consequent C is a single predicate.

For the following explanations we need a mathematical function supp(I), which is defined for a set I of predicates as a relative frequency of rows satisfying all predicates from I. For logical data, supp(I) equals to the relative frequency of rows, for which all predicates i_1, i_2, \ldots, i_n from I are TRUE. For numerical (double) input, supp(I) is computed as the mean (over all rows) of truth degrees of the formula ⁠i_1 AND i_2 AND ... AND i_n⁠, where AND is a triangular norm selected by the t_norm argument.

Association rules are characterized with the following quality measures.

Length of a rule is the number of elements in the antecedent.

Coverage of a rule is equal to supp(A).

Consequent support of a rule is equal to supp(\{c\}).

Support of a rule is equal to supp(A \cup \{c\}).

Confidence of a rule is the fraction supp(A) / supp(A \cup \{c\}).

Usage

dig_implications(
  x,
  antecedent = everything(),
  consequent = everything(),
  disjoint = var_names(colnames(x)),
  min_length = 0L,
  max_length = Inf,
  min_coverage = 0,
  min_support = 0,
  min_confidence = 0,
  contingency_table = FALSE,
  measures = NULL,
  t_norm = "goguen",
  threads = 1,
  ...
)

Arguments

x	a matrix or data frame with data to search in. The matrix must be numeric (double) or logical. If x is a data frame then each column must be either numeric (double) or logical.
antecedent	a tidyselect expression (see tidyselect syntax) specifying the columns to use in the antecedent (left) part of the rules
consequent	a tidyselect expression (see tidyselect syntax) specifying the columns to use in the consequent (right) part of the rules
disjoint	an atomic vector of size equal to the number of columns of x that specifies the groups of predicates: if some elements of the disjoint vector are equal, then the corresponding columns of x will NOT be present together in a single condition. If x is prepared with partition(), using the var_names() function on x's column names is a convenient way to create the disjoint vector.
min_length	the minimum length, i.e., the minimum number of predicates in the antecedent, of a rule to be generated. Value must be greater or equal to 0. If 0, rules with empty antecedent are generated in the first place.
max_length	The maximum length, i.e., the maximum number of predicates in the antecedent, of a rule to be generated. If equal to Inf, the maximum length is limited only by the number of available predicates.
min_coverage	the minimum coverage of a rule in the dataset x. (See Description for the definition of coverage.)
min_support	the minimum support of a rule in the dataset x. (See Description for the definition of support.)
min_confidence	the minimum confidence of a rule in the dataset x. (See Description for the definition of confidence.)
contingency_table	a logical value indicating whether to provide a contingency table for each rule. If TRUE, the columns pp, pn, np, and nn are added to the output table. These columns contain the number of rows satisfying the antecedent and the consequent, the antecedent but not the consequent, the consequent but not the antecedent, and neither the antecedent nor the consequent, respectively.
measures	a character vector specifying the additional quality measures to compute. If NULL, no additional measures are computed. Possible values are "lift", "conviction", "added_value". See https://mhahsler.github.io/arules/docs/measures for a description of the measures.
t_norm	a t-norm used to compute conjunction of weights. It must be one of "goedel" (minimum t-norm), "goguen" (product t-norm), or "lukas" (Lukasiewicz t-norm).
threads	the number of threads to use for parallel computation.
...	Further arguments, currently unused.

Value

A tibble with found patterns and computed quality measures.

Author(s)

Michal Burda

See Also

partition(), var_names(), dig()

Examples

d <- partition(mtcars, .breaks = 2)
dig_associations(d,
                 antecedent = !starts_with("mpg"),
                 consequent = starts_with("mpg"),
                 min_support = 0.3,
                 min_confidence = 0.8,
                 measures = c("lift", "conviction"))

nuggets documentation built on April 3, 2025, 8:07 p.m.