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.).
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))Indices of fuzzy attributes that may appear on the left-hand-side (LHS) of association rules, i.e. in the antecedent.
Indices of fuzzy attributes that may appear on the right-hand-side (RHS) of association rules, i.e. in the consequent.
A t-norm to be used for computation of conjunction of fuzzy attributes. (Allowed are even only starting letters of "lukasiewicz", "goedel" and "goguen").
The non-negative number of rules to be found. If zero, the function returns all rules
satisfying the given conditions. If positive, only n best rules are returned. The criterium
of what is ``best'' is specified with the best argument.
Specifies measure accordingly to which the rules are ordered from best to worst. This
argument is used mainly in combination with the n argument. Currently, only single value
("confidence") can be used.
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.
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.
Maximum confidence threshold. After finding a rule that has confidence degree
above the maxConfidence threshold, no other rule is resulted based on adding some additional
attribute to its antecedent part. I.e. if "Sm.age & Me.age => Sm.height" has confidence above
maxConfidence threshold, no another rule containing "Sm.age & Me.age" will be produced
regardless of its interest measures.
If you want to disable this feature, set maxConfidence to 1.
Maximum allowed length of the antecedent, i.e. maximum number of predicates that are allowed on the left-hand side of the rule. If negative, the maximum length of rules is unlimited.
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 searchrules function with some of the external parallelization mechanisms such as
library doMC, make sure that numThreads equals 1.
This feature is available only on systems that have installed the OpenMP library.
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 maxConfidence threshold.
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 n is set to 0) can be very
high if trie is set to FALSE.
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 t-norm 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:
supporta rule's support degree: \(1/nrow(d) * \sum_{\forall i} a(i) \otimes b(i) \otimes c(i)\)
lhsSupporta support of rule's antecedent (LHS): \(1/nrow(d) * \sum_{\forall i} a(i) \otimes b(i)\)
rhsSupporta support of rule's consequent (RHS): \(1/nrow(d) * \sum_{\forall i} c(i)\)
confidencea rule's confidence degree: \(support / lhsSupport\)
The function searches data frame d for fuzzy association rules that satisfy conditions
specified by the parameters.
# NOT RUN {
d <- lcut3(CO2)
searchrules(d, lhs=1:ncol(d), rhs=1:ncol(d))
# }
Run the code above in your browser using DataLab