arules (version 1.6-8)

rules-class: Class rules --- A Set of Rules

Description

The rules class represents a set of rules.

Objects from the Class

Objects are the result of calling the function apriori. Objects can also be created by calls of the form new("rules", ...).

Slots

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)

Extends

Class '>associations, directly.

Methods

% \item{[}{\code{signature(x = "rules")}; % extracts a subset of rules and the associated quality measures}

coerce

signature(from = "rules", to = "data.frame"); represents the set of rules as a data.frame

generatingItemsets

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.

itemInfo

signature(object = "rules"); returns the whole item information data frame including item labels

itemLabels

signature(object = "rules"); returns the item labels used to encode the rules

items

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

itemLabels

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.

labels

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

% \item{length}{\code{signature(x = "rules")}; % returns the number of rules stored in the the set}

lhs

signature(x = "rules"); returns the '>itemMatrix representing the left-hand-side of the rules (antecedents)

lhs<-

signature(x = "rules"); replaces the '>itemMatrix representing the left-hand-side of the rules (antecedents)

nitems

signature(x = "rules"); number of all possible items in the binary matrix representation of the object.

rhs

signature(x = "rules"); returns the '>itemMatrix representing the right-hand-side of the rules (consequents)

rhs<-

signature(x = "rules"); replaces the '>itemMatrix representing the right-hand-side of the rules (consequents)

%\item{subset}{\code{signature(x = "rules")}; % selects a subset using restrictions on the quality measures or on % the items present in the rules (see examples).}

summary

signature(object = "rules")

Details

Rules are usually created by calling an association rule mining algorithm like apriori. Rules store the LHS and the RHS separately as objects of class itemMatrix.

To create rules manually, 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.

See Also

associations-class, [-methods, apriori, c, duplicated, inspect, itemCoding length, match, sets, size, subset,

Examples

# NOT RUN {
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)
# }