arules (version 1.7-9)

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, ...)

Value

Returns an object of class itemsets.

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.

...

Additional arguments are added for convenience to the parameter list.

Author

Michael Hahsler and Bettina Gruen

Details

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

Eclat can also return the transaction IDs for each found itemset using tidLists = TRUE as a parameter and the result can be retrieved as a tidLists object with method tidLists() for class itemsets. Note that 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().

ruleInduction() can be used to generate rules from the found itemsets.

A weighted version of ECLAT is available as function weclat(). This version can be used to perform weighted association rule mining (WARM).

References

Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, and Wei Li. (1997) New algorithms for fast discovery of association rules. KDD'97: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, August 1997, Pages 283-286.

Christian Borgelt (2003) Efficient Implementations of Apriori and Eclat. Workshop of Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL, USA).

ECLAT Implementation: https://borgelt.net/eclat.html

See Also

Other mining algorithms: APappearance-class, AScontrol-classes, ASparameter-classes, apriori(), fim4r(), ruleInduction(), weclat()

Examples

data("Adult")
## Mine itemsets with minimum support of 0.1 and 5 or less items
itemsets <- eclat(Adult,
  parameter = list(supp = 0.1, maxlen = 5)
)
itemsets

## Create rules from the frequent itemsets
rules <- ruleInduction(itemsets, confidence = .9)
rules