eclat {arules} | R Documentation |
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. 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).
ECLAT Implementation: http://www.borgelt.net/eclat.html
ECparameter-class
,
ECcontrol-class
,
transactions-class
,
itemsets-class
,
weclat
,
apriori
,
ruleInduction
,
supportingTransactions
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