dig_tautologies: Find tautologies or "almost tautologies" in a dataset
In nuggets: Extensible Framework for Data Pattern Exploration

dig_tautologies

R Documentation

Find tautologies or "almost tautologies" in a dataset

Description

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,
  contingency_table = FALSE,
  measures = deprecated(),
  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.)
`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`	(Deprecated. Search for tautologies using `dig_tautologies(contingency_table = TRUE)` and use the `add_interest()` function on the result to compute additional 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"` (Łukasiewicz 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.

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.

Value

An S3 object which is an instance of associations and nugget classes and which is a tibble with found tautologies in the format equal to the output of dig_associations().