LUCS_KDD_CBA | R Documentation |
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.
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 ) install_LUCS_KDD_CPAR( force = FALSE, source = "https://cgi.csc.liv.ac.uk/~frans/KDD/Software/FOIL_PRM_CPAR/foilPrmCpar.tgz" ) install_LUCS_KDD_CMAR( force = FALSE, source = "https://cgi.csc.liv.ac.uk/~frans/KDD/Software/CMAR/cmar.tgz" )
formula |
a symbolic description of the model to be fitted. Has to be
of form |
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: |
verbose |
Show verbose output? |
support, confidence |
minimum support and minimum confidence thresholds for CMAR (range [0, 1]). |
force |
logical; force redownload, rebuilding and reinstallation? |
source |
source for the code. A local file can be specified as a URI
starting with |
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.
Returns an object of class CBA.object
representing the
trained classifier.
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/
## 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)
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