arulesCBA (version 1.2.3)

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 is only free for **non-commercial use**.

Usage

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

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.

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?

support, confidence

minimum support and minimum confidence thresholds for CMAR (range [0, 1]).

Value

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

Details

**Requirement:** The code needs a **JDK (Java Software Development Kit) Version 1.8 (or higher)** installation. A **headless Java installation is not sufficient.** 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.

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. 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 {
# make sure you have a Java SDK Version 1.4.0+ and not a headless installation.
system("java -version")

data("iris")

# 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))

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

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

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