rules-class {arules} | R Documentation |
The rules
class represents a set of rules.
Note that the class can also represent a multiset of rules with duplicated
elements. Duplicated elements can be removed with unique
.
Objects are the result of calling the function apriori
.
Objects can also be created by calls of the form
new("rules", ...)
.
lhs
:Object of class
itemMatrix
;
the left-hand-sides of the rules (antecedents)
rhs
:Object of class
itemMatrix
;
the right-hand-sides of the rules (consequents)
quality
:a data.frame
Class associations
, directly.
signature(from = "rules", to = "data.frame")
;
represents the set of rules as a data.frame
signature(object = "rules")
;
returns the whole item information data frame including item
labels
signature(object = "rules")
;
returns the item labels used to encode the rules
signature(x = "rules")
;
returns for each rule the union of the items in the
lhs and rhs (i.e., the itemsets
which generated the rule) as an
itemMatrix
signature(x = "rules")
;
returns a collection of the itemsets which generated the rules (one
itemset for each rule). Note that the collection can be a multiset and
contain duplicated
elements. Use unique
to remove duplicates and obtain a
proper set.
signature(object = "rules")
;
returns labels for the rules ("lhs => rhs") as a
character
vector. The representation can be customized using
the additional parameter ruleSep
and parameters for label
defined in itemMatrix
signature(object = "rules")
;
returns the item labels as a character vector.
The index for each label is the column index of the item in the
binary matrix.
signature(x = "rules")
;
returns the itemMatrix
representing the left-hand-side of the rules (antecedents)
signature(x = "rules")
;
replaces the itemMatrix
representing the left-hand-side of the rules (antecedents)
signature(x = "rules")
;
returns the itemMatrix
representing the right-hand-side of the rules (consequents)
signature(x = "rules")
;
replaces the itemMatrix
representing the right-hand-side of the rules (consequents)
signature(object = "rules")
[-methods
,
apriori
,
c
,
duplicated
,
inspect
,
length
,
match
,
sets
,
size
,
subset
,
associations-class
,
itemMatrix-class
,
data("Adult") ## Mine rules. rules <- apriori(Adult, parameter = list(support = 0.4)) ## Select a subset of rules using partial matching on the items ## in the right-hand-side and a quality measure rules.sub <- subset(rules, subset = rhs %pin% "sex" & lift > 1.3) ## Display rules. inspect(SORT(rules.sub)[1:3])