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()).
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
)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().
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.
a tidyselect expression (see tidyselect syntax) specifying the columns to use in the antecedent (left) part of the rules
a tidyselect expression (see tidyselect syntax) specifying the columns to use in the consequent (right) part of the rules
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.
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.
the minimum coverage of a rule in the dataset x.
(See Description for the definition of coverage.)
the minimum support of a rule in the dataset x.
(See Description for the definition of support.)
the minimum confidence of a rule in the dataset x.
(See Description for the definition of confidence.)
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.
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).
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.
a logical value indicating whether to print progress messages.
the number of threads to use for parallel computation.
Michal Burda
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.