D.discretize.equal.intervals.RST {RoughSets}

Unsupervised discretization into intervals of equal length.

Description

This function implements unsupervised discretization into intervals of equal size.

Usage

D.discretize.equal.intervals.RST(decision.table, nOfIntervals = 4)

Arguments

decision.table

an object inheriting from the "DecisionTable" class, which represents a decision system. See SF.asDecisionTable.

nOfIntervals

a positive integer giving the number of intervals.

Details

This approach belongs to a class of unsupervised discretization methods since it does not consider the class labels. Each numeric attribute is divided in k intervals of equal length. Detailed information regarding this method can be found in (Dougherty et al, 1995).

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.

Value

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

Author(s)

Andrzej Janusz

References

J. Dougherty, R. Kohavi, and M. Sahami, "Supervised and Unsupervised Discretization of Continuous Features", In A. Prieditis & S. J. Russell, eds. Work. Morgan Kaufmann, p. 194-202 (1995).

See Also

D.discretize.quantiles.RST, D.global.discernibility.heuristic.RST, D.local.discernibility.heuristic.RST, SF.applyDecTable. A wrapper function for all available discretization methods: D.discretization.RST

Examples

#################################################################
## Example: Determine cut values and generate new decision table
#################################################################
data(RoughSetData)
wine.data <- RoughSetData$wine.dt
cut.values <- D.discretize.equal.intervals.RST(wine.data, nOfIntervals = 3)

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