arules (version 1.0-12)

ruleInduction: Rule Induction for a Set of Itemsets

Description

Provides the generic function and the needed S4 method to induce all rules which can be generated by the given itemsets from a transactions data set.

Usage

ruleInduction(x, ...)
## S3 method for class 'itemsets':
ruleInduction(x, transactions, confidence = 0.8, 
    control = NULL)

Arguments

x

the set of itemsets from which rules will be induced.

...

further arguments.

transactions

the transaction data set used to mine the itemsets.

confidence

a numeric value giving the minimum confidence for the rules.

control

a named list with elements method indicating the method ("apriori" or "ptree"), and the logical arguments reduce and verbose to indicate if unused items are removed and if

Value

• An object of class rules.

Details

If in control method = "apriori" is used, a very simple rule induction method is used. All rules are mined from the transactions data set using Apriori with the minimal support found in itemsets. And in a second step all rules which do not stem from one of the itemsets are removed. This procedure will be in many cases very slow (e.g., for itemsets with many elements or very low support).

If in control method = "ptree" is used, the transactions are counted into a prefix tree and then the rules are selectively generated using the counts in the tree. This is usually faster than the above approach.

If in control reduce = TRUE is used, unused items are removed from the data before creating rules. This might be slower for large transaction data sets. However, if method = "ptree" this is highly recommended as the items are further reordered to reduce the counting time.

If argument transactions is missing it is assumed that x contains a lattice (complete set) of frequent itemsets together with their support counts. Then rules can be induced directly without support counting. This approach is very fast.

References

Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association rule generation. Computational Statistics, 23(2):303-315, April 2008

See Also

itemsets-class, rules-class transactions-class

Examples

data("Adult")

## find all closed frequent itemsets
closed <- apriori(Adult, 
	parameter = list(target = "closed", support = 0.4))

## rule induction
rules <- ruleInduction(closed, Adult, control = list(verbose = TRUE))
summary(rules)

## inspect the resulting rules
inspect(sort(rules, by = "lift")[1:5])

## use lattice of frequent itemsets
ec  <- eclat(Adult, parameter = list(support = 0.4))
rec <- ruleInduction(ec)
inspect(rec[1:5])