# is.redundant

0th

Percentile

##### Find Redundant Rules

Provides the generic functions and the S4 method is.redundant to find redundant rules.

Keywords
manip
##### Usage
is.redundant(x, ...)
# S4 method for rules
is.redundant(x, measure = "confidence")
##### Arguments
x

a set of rules.

measure

measure used to check for redundancy.

...

##### Details

A rule is redundant if a more general rules with the same or a higher confidence exists. That is, a more specific rule is redundant if it is only equally or even less predictive than a more general rule. A rule is more general if it has the same RHS but one or more items removed from the LHS. Formally, a rule $X \Rightarrow Y$ is redundant if

$$\exists X' \subset X \quad conf(X' \Rightarrow Y) \ge conf(X \Rightarrow Y).$$

This is equivalent to a negative or zero improvement as defined by Bayardo et al. (2000). In this implementation other measures than confidence, e.g. improvement of lift, can be used as well.

##### Value

returns a logical vector indicating which rules are redundant.

##### References

Bayardo, R. , R. Agrawal, and D. Gunopulos (2000). Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery, 4(2/3):217--240.

interestMeasure

##### Aliases
• is.redundant
• is.redundant,rules-method
##### Examples
# NOT RUN {
data("Income")

## mine some rules with the consequent "language in home=english"
rules <- apriori(Income, parameter = list(support = 0.5),
appearance = list(rhs = "language in home=english", default = "lhs"))

## for better comparison we sort the rules by confidence and add Bayado's improvement
rules <- sort(rules, by = "confidence")
quality(rules)\$improvement <- interestMeasure(rules, measure = "improvement")
inspect(rules)
is.redundant(rules)

## redundant rules
inspect(rules[is.redundant(rules)])

## non-redundant rules
inspect(rules[!is.redundant(rules)])
# }

Documentation reproduced from package arules, version 1.5-4, License: GPL-3

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