RoughSets (version 1.2-1)

BC.discernibility.mat.RST: 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 A.Introduction-RoughSets.

Usage

BC.discernibility.mat.RST(decision.table, range.object = NULL,
  return.matrix = 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.

Value

• An object of a class "DiscernibilityMatrix" which has the following components:
  • disc.mat: the decision-relative discernibility matrix which for pairs of objects from different decision classes stores names of attributes which can be used to discern between them. Only pairs of objects from different decision classes are considered. For other pairs thedisc.matcontainsNAvalues. Moreover, since the classical discernibility matrix is symmetric only the pairs from the lower triangular part are considered.
  • disc.list: a list containing unique clauses from the CNF representation of the discernibility function,
  • discernibility.type: a type of discernibility relation used in the computations,
  • type.model: a character vector identifying the type of model which was used. In this case, it is"RST"which means the rough set theory.

References

A. Skowron and C. Rauszer, "The Discernibility Matrices and Functions in Information Systems", in: R. Słowinski (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