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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", ...)
.
signature(from = "rules", to = "data.frame")
;
represents the set of rules as a data.frame
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. Technically this method produces the same as the
result of method items()
,
but wrapped into an
'>itemsets
object with support information.
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(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(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(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")
; number of all possible items in the
binary matrix representation of the object.
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
,
# NOT RUN {
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 the top 3 support rules
inspect(head(rules.sub, n = 3, by = "support"))
## Display the first 3 rules
inspect(rules.sub[1:3])
## Get labels for the first 3 rules
labels(rules.sub[1:3])
labels(rules.sub[1:3], itemSep = " + ", setStart = "", setEnd="",
ruleSep = " ---> ")
# }
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