Weka_associators {RWeka} | R Documentation

## R/Weka Associators

### Description

R interfaces to Weka association rule learning algorithms.

### Usage

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

### 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.

### 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.

### 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.

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))

    ## Tertius with defaults.
    Tertius(x)
    ## Some options: only classification rules (single item in the RHS).
    Tertius(x, Weka_control(S = TRUE))

[Package RWeka version 0.4-1 Index]