arules (version 1.0-12)

eclat: Mining Associations with Eclat

Description

Mine frequent itemsets with the Eclat algorithm. This algorithm uses simple intersection operations for equivalence class clustering along with bottom-up lattice traversal.

Usage

eclat(data, parameter = NULL, control = NULL)

Arguments

data

object of class transactions or any data structure which can be coerced into transactions (e.g., binary matrix, data.frame).

parameter

object of class ECparameter or named list (default values are: support 0.1 and maxlen 5)

control

object of class ECcontrol or named list for algorithmic controls.

Value

• Returns an object of class itemsets.

Details

Calls the C implementation of the Eclat algorithm by Christian Borgelt for mining frequent itemsets.

Note for contriol parameter tidLists=TRUE: Since storing transaction ID lists is very memory intensive, creating transaction ID lists only works for minimum support values which create a relatively small number of itemsets. See also supportingTransactions().

References

Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, and Wei Li. (1997) New algorithms for fast discovery of association rules. Technical Report 651, Computer Science Department, University of Rochester, Rochester, NY 14627. Christian Borgelt (2003) Efficient Implementations of Apriori and Eclat. Workshop of Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL, USA).

See Also

ECparameter-class, ECcontrol-class, transactions-class, itemsets-class, apriori, supportingTransactions

Examples

data("Adult")
## Mine itemsets with minimum support of 0.1.
itemsets <- eclat(Adult,
                  parameter = list(supp = 0.1, maxlen = 15))