ruleInduction {arules} | R Documentation |
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.
ruleInduction(x, ...) ## S4 method for signature 'itemsets' ruleInduction(x, transactions, confidence = 0.8, control = NULL)
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
|
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.
An object of class rules
.
Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association rule generation. Computational Statistics, 23(2):303-315, April 2008
itemsets-class
, rules-class
transactions-class
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])