RWeka (version 0.4-44)

Weka_associators: R/Weka Associators

Description

R interfaces to Weka association rule learning algorithms.

Usage

Apriori(x, control = NULL)
Tertius(x, control = NULL)

Value

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.

Arguments

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.

Details

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.

References

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. tools:::Rd_expr_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.

Examples

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))

if (FALSE) {
## 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))
}