R: Mining Associations with Eclat

eclat {arules} | R Documentation

## 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.

### 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).

### Value

Returns an object of class `itemsets`.

### Author(s)

Michael Hahsler and Bettina Gruen

### 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

`ECparameter-class`, `ECcontrol-class`, `transactions-class`, `itemsets-class`, `weclat`, `apriori`, `ruleInduction`, `supportingTransactions`

### 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 itemsets
    rules <- ruleInduction(itemsets, Adult, confidence = .9)
    rules

[Package arules version 1.6-7 Index]