Class rules — A Set of Rules
The rules class represents a set of rules.
Rules are usually created by calling an assoication rule mining algorithm like apriori.
Rules store the LHS and the RHS separately as objects of class itemMatrix.
To create rules manualy, the itemMatrix for the LHS and the RHS
of the rules can be created using itemCoding.
Note the two matrices
need to have the itemLabels (i.e., columns of the sparse matrix) in the same order.
An example is in the Example section below.
Mined rule sets typically contain several interest measures accessible with the quality method.
Additional measures can be calculated via interestMeasure.
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; typically contains measures like support, confidence and count (i.e., the absolute support count)
Class associations, directly.
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")
Michael Hahsler
associations-class,
[-methods,
apriori,
c,
duplicated,
inspect,
itemCoding
length,
match,
sets,
size,
subset,
data("Adult")
## Mine rules
rules <- apriori(Adult, parameter = list(support = 0.3))
rules
## 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 = " ---> ")
## Create a rules for the Adult dataset manually and calcualte some interest Measures
twoRule <- new("rules",
lhs = encode(list(
c("age=Young", "relationship=Unmarried"),
c("age=Old")
), itemLabels = itemLabels(Adult)),
rhs = encode(list(
c("income=small"),
c("income=large")
), itemLabels = itemLabels(Adult))
)
quality(twoRule) <- interestMeasure(twoRule,
measure = c("support", "confidence", "lift"), transactions = Adult)
inspect(twoRule)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.