RoughSets (version 1.2-1)

D.global.discernibility.heuristic.RST: Supervised discretization based on the maximum discernibility heuristic

Description

It is a function used for computing globally semi-optimal cuts using the maximum discernibility heuristic.

Usage

D.global.discernibility.heuristic.RST(decision.table, maxNOfCuts = 2 *
  ncol(decision.table), attrSampleSize = ncol(decision.table) - 1,
  cutCandidatesList = NULL, discFunction = global.discernibility, ...)

Arguments

decision.table

an object inheriting from the "DecisionTable" class, which represents a decision system. See SF.asDecisionTable. It should be noted that especially for this method, all conditional a

maxNOfCuts

a positive integer indicating the maximum number of allowed cuts.

attrSampleSize

an integer between 1 and the number of conditional attributes (the default). It indicates the attribute sample size for the Monte Carlo selection of candidating cuts.

cutCandidatesList

an optional list containing candidates for optimal cut values.

discFunction

a function used for computation of cuts. Currently only one implementation of maximu discernibility heuristic is available (the default). However, this parameter can be used to integrate custom implementations of discretization functions with the Ro

...

additional parameters to the discFunction (currently unsupported).

Value

• An object of a class "Discretization" which stores cuts for each conditional attribute. See D.discretization.RST.

Details

A complete description of the implemented algorithm can be found in (Nguyen, 2001).

It should be noted that the output of this function is an object of a class "Discretization" which contains the cut values. The function SF.applyDecTable has to be used in order to generate the new (discretized) decision table.

References

S. H. Nguyen, "On Efficient Handling of Continuous Attributes in Large Data Bases", Fundamenta Informaticae, vol. 48, p. 61 - 81 (2001).

See Also

D.discretize.quantiles.RST, D.discretize.equal.intervals.RST and SF.applyDecTable.

Examples

#################################################################
## Example: Determine cut values and generate new decision table
#################################################################
data(RoughSetData)
wine.data <- RoughSetData$wine.dt
cut.values <- D.global.discernibility.heuristic.RST(wine.data)

## generate a new decision table:
wine.discretized <- SF.applyDecTable(wine.data, cut.values)
dim(wine.discretized)
lapply(wine.discretized, unique)

## remove attributes with only one possible value:
to.rm.idx <- which(sapply(lapply(wine.discretized, unique), function(x) length(x) == 1))
to.rm.idx
wine.discretized.reduced <- wine.discretized[-to.rm.idx]
dim(wine.discretized.reduced)

## check whether the attributes in the reduced data are a super-reduct of the original data:
colnames(wine.discretized.reduced)
class.idx <- which(colnames(wine.discretized.reduced) == "class")
sum(duplicated(wine.discretized.reduced)) == sum(duplicated(wine.discretized.reduced[-class.idx]))
## yes it is