BC.discernibility.mat.RST {RoughSets}R Documentation

Computation of a decision-relative discernibility matrix based on the rough set theory

Description

This function implements a fundamental part of RST: a decision-relative discernibility matrix. This notion was proposed by (Skowron and Rauszer, 1992) as a middle-step in many RST algorithms for computaion of reducts, discretization and rule induction. A more detailed explanation of this notion can be found in Introduction-RoughSets.

Usage

BC.discernibility.mat.RST(
  decision.table,
  range.object = NULL,
  return.matrix = FALSE,
  attach.data = FALSE
)

Arguments

decision.table

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

range.object

an integer vector indicating objects for construction of the k-relative discernibility matrix. The default value is NULL which means that all objects in the decision table are used.

return.matrix

a logical value determining whether the discernibility matrix should be retunred in the output. If it is set to FALSE (the default) only a list containing unique clauses from the CNF representation of the discernibility function is returned.

attach.data

a logical indicating whether the original decision table should be attached as an additional element of the resulting list named as dec.table.

Value

An object of a class DiscernibilityMatrix which has the following components:

Author(s)

Lala Septem Riza and Andrzej Janusz

References

A. Skowron and C. Rauszer, "The Discernibility Matrices and Functions in Information Systems", in: R. SÅ‚owinski (Ed.), Intelligent Decision Support: Handbook of Applications and Advances of Rough Sets Theory, Kluwer Academic Publishers, Dordrecht, Netherland, p. 331 - 362 (1992).

See Also

BC.IND.relation.RST, BC.LU.approximation.RST, BC.LU.approximation.FRST and BC.discernibility.mat.FRST

Examples

#######################################################################
## Example 1: Constructing the decision-relative discernibility matrix
#######################################################################
data(RoughSetData)
hiring.data <- RoughSetData$hiring.dt

## building the decision-relation discernibility matrix
disc.matrix <- BC.discernibility.mat.RST(hiring.data, return.matrix = TRUE)
disc.matrix


[Package RoughSets version 1.3-8 Index]