Weka_associators {RWeka} | R Documentation |
R interfaces to Weka association rule learning algorithms.
Apriori(x, control = NULL) Tertius(x, control = NULL)
x |
an R object with the data to be associated. |
control |
an object of class Weka_control , or a
character vector of control options, or NULL (default).
Available options can be obtained on-line using the Weka Option
Wizard WOW , or the Weka documentation. |
Apriori
implements an Apriori-type algorithm, which iteratively
reduces the minimum support until it finds the required number of
rules with the given minimum confidence.
Tertius
implements a Tertius-type algorithm.
See the references for more information on these algorithms.
A list inheriting from class Weka_associators
with components
including
associator |
a reference (of class
jobjRef ) to a Java object
obtained by applying the Weka buildAssociations method to the
training instances using the given control options. |
R. Agrawal and R. Srikant (1994). Fast algorithms for mining association rules in large databases. Proceedings of the International Conference on Very Large Databases, 478–499. Santiago, Chile: Morgan Kaufmann, Los Altos, CA.
P. A. Flach and N. Lachiche (1999). Confirmation-guided discovery of first-order rules with Tertius. Machine Learning, 42, 61–95.
I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
x <- read.arff(system.file("arff", "contact-lenses.arff", package = "RWeka")) ## Apriori with defaults. Apriori(x) ## Some options: set required number of rules to 20. Apriori(x, Weka_control(N = 20)) ## Tertius with defaults. Tertius(x) ## Some options: only classification rules (single item in the RHS). Tertius(x, Weka_control(S = TRUE))