Find tautologies or "almost tautologies" in a dataset

This function finds tautologies in a dataset, i.e., rules of the form {a1 & a2 & ... & an} => {c} where a1, a2, ..., an are antecedents and c is a consequent. The intent of searching for tautologies is to find rules that are always true, which may be used for filtering of further generated conditions. The resulting rules may be used as a basis for the list of excluded formulae (see the excluded argument of dig()).

Usage

dig_tautologies(
  x,
  antecedent = everything(),
  consequent = everything(),
  disjoint = var_names(colnames(x)),
  max_length = Inf,
  min_coverage = 0,
  min_support = 0,
  min_confidence = 0,
  measures = NULL,
  t_norm = "goguen",
  max_results = Inf,
  verbose = FALSE,
  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.
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.)
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).
max_results: the maximum number of generated conditions to execute the callback function on. If the number of found conditions exceeds max_results, the function stops generating new conditions and returns the results. To avoid long computations during the search, it is recommended to set max_results to a reasonable positive value. Setting max_results to Inf will generate all possible conditions.
verbose: a logical value indicating whether to print progress messages.
threads: the number of threads to use for parallel computation.

Value

A tibble with found tautologies in the format equal to the output of dig_associations().

Details

The search for tautologies is performed by iteratively searching for rules with increasing length of the antecedent. Rules found in previous iterations are used as excluded argument in the next iteration.

Author

Michal Burda