Description Usage Arguments Details Value Author(s) See Also Examples
This function searches the given fsets()
object d
for all
fuzzy association rules that satisfy defined constraints. It returns a list
of fuzzy association rules together with some statistics characterizing them
(such as support, confidence etc.).
1 2 3 4 5 6 7 8 9 10 11 12 13 14  searchrules(
d,
lhs = 2:ncol(d),
rhs = 1,
tnorm = c("goedel", "goguen", "lukasiewicz"),
n = 100,
best = c("confidence"),
minSupport = 0.02,
minConfidence = 0.75,
maxConfidence = 1,
maxLength = 4,
numThreads = 1,
trie = (maxConfidence < 1)
)

d 
An object of class 
lhs 
Indices of fuzzy attributes that may appear on the lefthandside (LHS) of association rules, i.e. in the antecedent. 
rhs 
Indices of fuzzy attributes that may appear on the righthandside (RHS) of association rules, i.e. in the consequent. 
tnorm 
A tnorm to be used for computation of conjunction of fuzzy attributes. (Allowed are even only starting letters of "lukasiewicz", "goedel" and "goguen"). 
n 
The nonnegative number of rules to be found. If zero, the function
returns all rules satisfying the given conditions. If positive, only

best 
Specifies measure accordingly to which the rules are ordered
from best to worst. This argument is used mainly in combination with the

minSupport 
The minimum support degree of a rule. Rules with support below that number are filtered out. It must be a numeric value from interval [0, 1]. See below for details on how the support degree is computed. 
minConfidence 
The minimum confidence degree of a rule. Rules with confidence below that number are filtered out. It must be a numeric value from interval [0, 1]. See below for details on how the confidence degree is computed. 
maxConfidence 
Maximum confidence threshold. After finding a rule that
has confidence degree above the If you want to disable this feature, set 
maxLength 
Maximum allowed length of the rule, i.e. maximum number of predicates that are allowed on the lefthand + righthand side of the rule. If negative, the maximum length of rules is unlimited. 
numThreads 
Number of threads used to perform the algorithm in
parallel. If greater than 1, the OpenMP library (not to be confused with
Open MPI) is used for parallelization. Please note that there are known
problems of using OpenMP together with another means of parallelization that
may be used within R. Therefore, if you plan to use the 
trie 
Whether or not to use internal mechanism of Tries. If FALSE,
then in the output may appear such rule that is a descendant of a rule that
has confidence above Tries consume very much memory, so if you encounter problems with
insufficient memory, set this argument to FALSE. On the other hand, the size
of result (if 
The function searches data frame d
for fuzzy association rules that
satisfy conditions specified by the parameters.
A list of the following elements: rules
and statistics
.
rules
is a list of mined fuzzy association rules. Each element of
that list is a character vector with consequent attribute being on the first
position.
statistics
is a data frame of statistical characteristics about mined
rules. Each row corresponds to a rule in the rules
list. Let us
consider a rule "a & b => c", let \otimes be a tnorm specified with
the tnorm
parameter and i goes over all rows of a data table
d
. Then columns of the statistics
data frame are as follows:
support: a rule's support degree: 1/nrow(d) * ∑_{\forall i} a(i) \otimes b(i) \otimes c(i)
lhsSupport: a support of rule's antecedent (LHS): 1/nrow(d) * ∑_{\forall i} a(i) \otimes b(i)
rhsSupport: a support of rule's consequent (RHS): 1/nrow(d) * ∑_{\forall i} c(i)
confidence: a rule's confidence degree: support / lhsSupport
Michal Burda
fcut()
, lcut()
, farules()
, fsets()
, pbld()
1 2  d < lcut(CO2)
searchrules(d, lhs=1:ncol(d), rhs=1:ncol(d))

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