arulesCBA: LUCS_KDD_CBA – R documentation

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LUCS_KDD_CBA

Interface to the LUCS-KDD Implementations of CMAR, PRM and CPAR

Description

Interface for the LUCS-KDD Software Library Java implementations of CMAR (Li, Han and Pei, 2001), PRM, and CPAR (Yin and Han, 2003). Note: The Java implementations is not part of arulesCBA and not covered by the packages license. It will be downloaded and compiled separately. It is available free of charge for non-commercial use.

Usage

CMAR(formula, data, support = 0.1, confidence = 0.5,
  disc.method = "mdlp", verbose = FALSE)
PRM(formula, data, best_k = 5, disc.method = "mdlp", verbose = FALSE)
CPAR(formula, data, best_k = 5, disc.method = "mdlp", verbose = FALSE)
FOIL2(formula, data, best_k = 5, disc.method = "mdlp", verbose = FALSE)

install_LUCS_KDD_CMAR(force = FALSE,
  source = "https://cgi.csc.liv.ac.uk/~frans/KDD/Software/CMAR/cmar.tgz")
install_LUCS_KDD_CPAR(force = FALSE,
  source = "https://cgi.csc.liv.ac.uk/~frans/KDD/Software/FOIL_PRM_CPAR/foilPrmCpar.tgz")

Arguments

`formula`	a symbolic description of the model to be fitted. Has to be of form `class ~ .` or `class ~ predictor1 + predictor2`.
`data`	A data.frame or a transaction set containing the training data. Data frames are automatically discretized and converted to transactions.
`support, confidence`	minimum support and minimum confidence thresholds for CMAR (range [0, 1]).
`best_k`	use average expected accuracy (laplace) of the best k rules per class for prediction.
`disc.method`	Discretization method used to discretize continuous variables if data is a data.frame (default: `"mdlp"`). See `discretizeDF.supervised` for more supervised discretization methods.
`verbose`	Show verbose output?
`force`	logical; force redownload, rebuilding and reinstallation?
`source`	source for the code. A local file can be specified as a URI starting with `file://` (see `download.file`).

Details

Installation: The LUCS-KDD code is not part of the package and has to be downloaded, compiled and installed using install_LUCS_KDD_CMAR() and install_LUCS_KDD_CPAR(). You need a complete Java JDK installation including the javac compiler. On some systems (Windows), you may need to set the JAVA_HOME environment variable so the system finds the compiler.

Memory: The memory for Java can be increased via R options. For example: options(java.parameters = "-Xmx1024m")

Note: The implementation does not expose the min. gain parameter for CPAR, PRM and FOIL2. It is fixed at 0.7 (the value used by Yin and Han, 2001). FOIL2 is an alternative Java implementation to the native implementation of FOIL already provided in the arulesCBA. FOIL exposes min. gain.

Value

Returns an object of class CBA.object representing the trained classifier.

References

Li W., Han, J. and Pei, J. CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules, ICDM, 2001, pp. 369-376.

Yin, Xiaoxin and Jiawei Han. CPAR: Classification based on Predictive Association Rules, SDM, 2003. doi: 10.1137/1.9781611972733.40

Frans Coenen et al. The LUCS-KDD Software Library, https://cgi.csc.liv.ac.uk/~frans/KDD/Software/

Examples

## Not run: 
data("iris")

# install and compile CMAR
install_LUCS_KDD_CMAR()

# build a classifier, inspect rules and make predictions
cl <- CMAR(Species ~ ., iris, support = .2, confidence = .8, verbose = TRUE)
cl

inspect(rules(cl))

predict(cl, head(iris))

# install CPAR (also installs PRM and FOIL2)
install_LUCS_KDD_CPAR()

cl <- CPAR(Species ~ ., iris)
cl

cl <- PRM(Species ~ ., iris)
cl

cl <- FOIL2(Species ~ ., iris)
cl

## End(Not run)

arulesCBA

Classification Based on Association Rules

v1.2.0

GPL-3

Authors

Michael Hahsler [aut, cre, cph], Ian Johnson [aut, cph], Tyler Giallanza [ctb]

Initial release

2020-4-17