mineCARs {arulesCBA}

Mine Class Association Rules

Description

Class Association Rules (CARs) are association rules that have only items with class values in the RHS as introduced for the CBA algorithm by Liu et al., 1998.

Usage

mineCARs(
  formula,
  transactions,
  parameter = NULL,
  control = NULL,
  balanceSupport = FALSE,
  verbose = TRUE,
  ...
)

Arguments

formula

A symbolic description of the model to be fitted.

transactions

An object of class arules::transactions containing the training data.

parameter, control

Optional parameter and control lists for arules::apriori().

balanceSupport

logical; if TRUE, class imbalance is counteracted by using class specific minimum support values. Alternatively, a support value for each class can be specified (see Details section).

verbose

logical; report progress?

...

For convenience, the mining parameters for arules::apriori() can be specified as .... Examples are the support and confidence thresholds, and the maxlen of rules.

Details

Class association rules (CARs) are of the form

P \Rightarrow c_i,

where the LHS P is a pattern (i.e., an itemset) and c_i is a single items representing the class label.

Mining parameters. Mining parameters for arules::apriori() can be either specified as a list (or object of arules::APparameter) as argument parameter or, for convenience, as arguments in .... Note: mineCARs() uses by default a minimum support of 0.1 (for the LHS of the rules via parameter originalSupport = FALSE), a minimum confidence of 0.5 and a maxlen (rule length including items in the LHS and RHS) of 5.

Balancing minimum support. Using a single minimum support threshold for a highly class imbalanced dataset will lead to the problem, that minority classes will only be presented in very few rules. To address this issue, balanceSupport = TRUE can be used to adjust minimum support for each class dependent on the prevalence of the class (i.e., the frequency of the c_i in the transactions) similar to the minimum class support suggested for CBA by Liu et al (2000) we use

minsupp_i = minsupp_t \frac{supp(c_i)}{max(supp(C))},

where max(supp(C)) is the support of the majority class. Therefore, the defined minimum support is used for the majority class and then minimum support is scaled down for classes which are less prevalent, giving them a chance to also produce a reasonable amount of rules. In addition, a named numerical vector with a support values for each class can be specified.

Value

Returns an object of class arules::rules.

Author(s)

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.

Liu B., Ma Y., Wong C.K. (2000) Improving an Association Rule Based Classifier. In: Zighed D.A., Komorowski J., Zytkow J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science, vol 1910. Springer, Berlin, Heidelberg.

See Also

Other preparation: CBA_ruleset(), discretizeDF.supervised(), prepareTransactions(), transactions2DF()

Examples

data("iris")

# discretize and convert to transactions
iris.trans <- prepareTransactions(Species ~ ., iris)

# mine CARs with items for "Species" in the RHS.
# Note: mineCars uses a default a minimum coverage (lhs support) of 0.1, a
#       minimum confidence of .5 and maxlen of 5
cars <- mineCARs(Species ~ ., iris.trans)
inspect(head(cars))

# specify minimum support and confidence
cars <- mineCARs(Species ~ ., iris.trans,
  parameter = list(support = 0.3, confidence = 0.9, maxlen = 3))
inspect(head(cars))

# for convenience this can also be written without a list for parameter using ...
cars <- mineCARs(Species ~ ., iris.trans, support = 0.3, confidence = 0.9, maxlen = 3)

# restrict the predictors to items starting with "Sepal"
cars <- mineCARs(Species ~ Sepal.Length + Sepal.Width, iris.trans)
inspect(cars)

# using different support for each class
cars <- mineCARs(Species ~ ., iris.trans, balanceSupport = c(
  "Species=setosa" = 0.1,
  "Species=versicolor" = 0.5,
  "Species=virginica" = 0.01), confidence = 0.9)
cars

# balance support for class imbalance
data("Lymphography")
Lymphography_trans <- as(Lymphography, "transactions")

classFrequency(class ~ ., Lymphography_trans)

# mining does not produce CARs for the minority classes
cars <- mineCARs(class ~ ., Lymphography_trans, support = .3, maxlen = 3)
classFrequency(class ~ ., cars, type = "absolute")

# Balance support by reducing the minimum support for minority classes
cars <- mineCARs(class ~ ., Lymphography_trans, support = .3, maxlen = 3,
  balanceSupport = TRUE)
classFrequency(class ~ ., cars, type = "absolute")

# Mine CARs from regular transactions (a negative class item is automatically added)
data(Groceries)
cars <- mineCARs(`whole milk` ~ ., Groceries,
  balanceSupport = TRUE, support = 0.01, confidence = 0.8)
inspect(sort(cars, by = "lift"))