dig_implications {nuggets} | R Documentation |
Search for implicative rules
Description
Implicative rule is a rule of the form ,
where
(antecedent) is a set of predicates and
(consequent) is a predicate.
Usage
dig_implications(
x,
antecedent = everything(),
consequent = everything(),
disjoint = NULL,
min_length = 0L,
max_length = Inf,
min_coverage = 0,
min_support = 0,
min_confidence = 0,
t_norm = "goguen",
...
)
Arguments
x |
a matrix or data frame with data to search in. The matrix must be
numeric (double) or logical. If |
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 |
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 |
min_support |
the minimum support of a rule in the dataset |
min_confidence |
the minimum confidence of a rule in the dataset |
t_norm |
a t-norm used to compute conjunction of weights. It must be one of
|
... |
Further arguments, currently unused. |
Details
For the following explanations we need a mathematical function , which
is defined for a set
of predicates as a relative frequency of rows satisfying
all predicates from
. For logical data,
equals to the relative
frequency of rows, for which all predicates
from
are TRUE.
For numerical (double) input,
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.
Implicative 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 .
Support of a rule is equal to .
Confidence of a rule is the fraction .
Value
A tibble with found rules and computed quality measures.
Author(s)
Michal Burda