apriori {arules} | R Documentation |
Mine frequent itemsets, association rules or association hyperedges using the Apriori algorithm. The Apriori algorithm employs level-wise search for frequent itemsets. The implementation of Apriori used includes some improvements (e.g., a prefix tree and item sorting).
apriori(data, parameter = NULL, appearance = NULL, control = NULL)
data |
object of class
|
parameter |
object of class
|
appearance |
object of class
|
control |
object of class
|
Calls the C implementation of the Apriori algorithm by Christian Borgelt for mining frequent itemsets, rules or hyperedges.
Note: Apriori only creates rules with one item in the RHS (Consequent)!
Note: The default value in APparameter
for minlen
is 1. This
means that rules with only one item (i.e., an empty antecedent/LHS) like
{} => {beer}
will be created.
These rules mean that no matter what other items are involved the
item in the RHS will appear with the probability given by the rule's
confidence (which equals the support).
If you want to avoid these rules then use
the argument parameter=list(minlen=2)
.
Returns an object of class rules
or
itemsets
.
R. Agrawal, T. Imielinski, and A. Swami (1993) Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 207–216, Washington D.C.
Christian Borgelt and Rudolf Kruse (2002) Induction of Association Rules: Apriori Implementation. 15th Conference on Computational Statistics (COMPSTAT 2002, Berlin, Germany) Physica Verlag, Heidelberg, Germany.
Christian Borgelt (2003) Efficient Implementations of Apriori and Eclat. Workshop of Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL, USA).
APparameter-class
,
APcontrol-class
,
APappearance-class
,
transactions-class
,
itemsets-class
,
rules-class
data("Adult") ## Mine association rules. rules <- apriori(Adult, parameter = list(supp = 0.5, conf = 0.9, target = "rules")) summary(rules)