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
transactions or any data structure
which can be coerced into
transactions (e.g., a binary
matrix or data.frame). |
parameter |
object of class
APparameter or named list.
The default behavior is to mine rules with support 0.1, confidence
0.8, and maxlen 5. |
appearance |
object of class
APappearance or named list.
With this argument item appearance can be restricted.
By default all items can appear unrestricted. |
control |
object of class
APcontrol or named list.
Controls the performance of the mining algorithm (item sorting, etc.) |
Calls the C implementation of the Apriori algorithm by Christian Borgelt for mining frequent itemsets, rules or hyperedges.
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)