arulesCBA: CBA – R documentation

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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)

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.
`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.
`...`	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.
`verbose`	Show progress?

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 standard APRIORI algorithm. Rules are ranked by confidence, support and size. Then either the M1 or M2 algorithm are used to perform database coverage pruning and to determin the number of rules to use and the default class.

Value

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

Author(s)

Ian Johnson and Michael Hahsler

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

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(rules(classifier))

# 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))

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

CBA