Mining Associations with Eclat
Mine frequent itemsets with the Eclat algorithm. This algorithm uses simple intersection operations for equivalence class clustering along with bottom-up lattice traversal.
eclat(data, parameter = NULL, control = NULL)
data |
object of class
|
parameter |
object of class
|
control |
object of class
|
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).
Returns an object of class itemsets
.
Michael Hahsler and Bettina Gruen
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
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
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