arulesCBA (version 1.2.7)

CBA: Classification Based on Association Rules Algorithm (CBA)

Description

Build a classifier based on association rules using the ranking, pruning and classification strategy of the CBA algorithm by Liu, et al. (1998).

Usage

CBA(
  formula,
  data,
  pruning = "M1",
  parameter = NULL,
  control = NULL,
  balanceSupport = FALSE,
  disc.method = "mdlp",
  verbose = FALSE,
  ...
)
pruneCBA_M1(formula, rules, transactions, verbose = FALSE)
pruneCBA_M2(formula, rules, transactions, verbose = FALSE)

Value

Returns an object of class CBA representing the trained classifier.

Arguments

formula

A symbolic description of the model to be fitted. Has to be of form class ~ . or class ~ predictor1 + predictor2.

data

arules::transactions containing the training data or a data.frame which. is automatically discretized and converted to transactions with prepareTransactions().

pruning

Pruning strategy used: "M1" or "M2".

parameter, control

Optional parameter and control lists for apriori.

balanceSupport

balanceSupport parameter passed to mineCARs() function.

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 progress?

...

For convenience, additional parameters are used to create the parameter control list for apriori (e.g., to specify the support and confidence thresholds).

rules, transactions

prune a set of rules using a transaction set.

Author

Ian Johnson and Michael Hahsler

Details

Implementation the CBA algorithm with the M1 or M2 pruning strategy introduced by Liu, et al. (1998).

Candidate classification association rules (CARs) are mined with the APRIORI algorithm but minimum support is only checked for the LHS (rule coverage) and not the whole rule. Rules are ranked by confidence, support and size. Then either the M1 or M2 algorithm are used to perform database coverage pruning and default rule pruning.

References

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. https://dl.acm.org/doi/10.5555/3000292.3000305

See Also

Other classifiers: CBA_helpers, CBA_ruleset(), FOIL(), LUCS_KDD_CBA, RCAR(), RWeka_CBA

Examples

data("iris")

# 1. Learn a classifier using automatic default discretization
classifier <- CBA(Species ~ ., data = iris, supp = 0.05, conf = 0.9)
classifier

# inspect the rule base
inspect(classifier$rules)

# make predictions
predict(classifier, head(iris))
table(pred = predict(classifier, iris), true = iris$Species)


# 2. Learn classifier from transactions (and use verbose)
iris_trans <- prepareTransactions(Species ~ ., iris, disc.method = "mdlp")
iris_trans
classifier <- CBA(Species ~ ., data = iris_trans, supp = 0.05, conf = 0.9, verbose = TRUE)
classifier

# make predictions. Note: response extracts class information from transactions.
predict(classifier, head(iris_trans))
table(pred = predict(classifier, iris_trans), true = response(Species ~ ., iris_trans))