R/Weka Associators
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 |
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
|
Tertius requires Weka package tertius to be installed.
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. doi: 10.1023/A:1007656703224.
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))
## Not run:
## Requires Weka package 'tertius' to be installed.
## Tertius with defaults.
Tertius(x)
## Some options: only classification rules (single item in the RHS).
Tertius(x, Weka_control(S = TRUE))
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.