CBA {arulesCBA} | R Documentation |
Build a classifier based on association rules mined for an input dataset. The CBA algorithm used is a modified version of the algorithm described by Liu, et al. (1998).
CBA(formula, data, support = 0.2, confidence = 0.8, verbose=FALSE, parameter = NULL, control = NULL, sort.parameter = NULL, lhs.support = FALSE, disc.method = "mdlp")
formula |
A symbolic description of the model to be fitted. Has to be of form |
data |
A data.frame containing the training data. |
support, confidence |
Minimum support and confidence for creating association rules. |
verbose |
Optional logical flag to allow verbose execution, where additional intermediary execution information is printed at runtime. |
parameter, control |
Optional parameter and control lists for apriori. |
sort.parameter |
Ordered vector of arules interest measures (as characters) which are used to sort rules in preprocessing. |
lhs.support |
Logical variable, which, when set to TRUE, indicates that LHS support should be used for rule mining. lhs.support rule mining is considerably slower than normal mining. |
disc.method |
Discretization method for factorizing numeric input (default: |
Formats the input data frame and calls a C implementation of the CBA algorithm from Liu, et al. (1998) split up into three stages to build a classifier based on a set of association rules.
Before the CBA algorithm in C is executed, association rules are generated with the Apriori algorithm from the arules package.
A default class is selected for the classifier. Note that for datasets which do not yield any strong association rules it is possible that no rules will be included in the classifier, and only a default class.
Returns an object of class CBA.object
representing the trained classifier.
Ian Johnson
Liu, B. Hsu, W. and Ma, Y (1998). Integrating Classification and Association Rule Mining. KDD'98 Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, 27-31 August. AAAI. pp. 80-86.
CBA.object
,
discretizeDF.supervised
,
apriori
,
rules
,
transactions
.
data("iris") # learn a classifier using automatic default discretization classifier <- CBA(Species ~ ., data = iris, supp = 0.05, conf = 0.9) classifier # make predictions for the first few instances of iris predict(classifier, head(iris)) # inspect the rule base inspect(rules(classifier)) # learn classifier from transactions trans <- as(discretizeDF.supervised(Species ~ ., iris), "transactions") classifier <- CBA(Species ~ Sepal, data = trans, supp = 0.05, conf = 0.9) classifier predict(classifier, head(trans))