Weka_associators

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R/Weka Associators

R interfaces to Weka association rule learning algorithms.

Keywords
models
Usage
Apriori(x, control = NULL)
Tertius(x, control = NULL)
Arguments
x
an R object with the data to be associated.
control
a character vector with 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
  • associatora 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. Santiage, 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.

Aliases
  • Apriori
  • Tertius
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, c("-N", "20"))

## Tertius with defaults.
Tertius(x)
## Some options: only classification rules (single item in the RHS).
Tertius(x, "-S")
Documentation reproduced from package RWeka, version 0.1-0, License: GPL version 2 or newer

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