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
method indicating the method ("apriori" or "ptree" ),
and the logical arguments reduce and
verbose to indicate if unused items are removed and if
the output should be verbose.
Currently, "ptree" is the default method. |
For 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 read into a prefix tree and
then the rules are selectively generated using the counts in the tree.
This is normally faster, but the code is not yet optimized.
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.
An object of class rules
.
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)) ### inspect the resulting rules inspect(SORT(rules, by = "lift")[1:5])