Find Redundant Rules
Provides the generic functions and the S4 method is.redundant
to find redundant rules.
is.redundant(x, ...) ## S4 method for signature 'rules' is.redundant(x, measure = "confidence")
x |
a set of rules. |
measure |
measure used to check for redundancy. |
... |
additional arguments. |
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 -> Y is redundant if
for some X' subset X, conf(X' -> Y) >= conf(X -> 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.
returns a logical vector indicating which rules are redundant.
Michael Hahsler and Christian Buchta
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.
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)])Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.