Description Usage Arguments Value Author(s) References See Also Examples
View source: R/BasicRoughSets.R
This function implements a fundamental part of RST: computation of a positive region and the
degree of dependency. This function can be used as a basic building block for development
of other RSTbased methods. A more detailed explanation of this notion can be found
in A.IntroductionRoughSets
.
1  BC.positive.reg.RST(decision.table, roughset)

decision.table 
an object inheriting from the 
roughset 
an object inheriting from the 
An object of a class "PositiveRegion"
which is a list with the following components:
positive.reg
: an integer vector containing indices of data instances belonging
to the positive region,
degree.dependency
: a numeric value giving the degree of dependency,
type.model
: a varacter vector identifying the utilized model. In this case,
it is "RST"
which means the rough set theory.
Andrzej Janusz
Z. Pawlak, "Rough Sets", International Journal of Computer and Information Sciences, vol. 11, no. 5, p. 341  356 (1982).
BC.IND.relation.RST
, BC.LU.approximation.RST
, BC.LU.approximation.FRST
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ########################################################
data(RoughSetData)
hiring.data < RoughSetData$hiring.dt
## We select a single attribute for computation of indiscernibility classes:
A < c(2)
## compute the indiscernibility classes:
IND.A < BC.IND.relation.RST(hiring.data, feature.set = A)
## compute the lower and upper approximation:
roughset < BC.LU.approximation.RST(hiring.data, IND.A)
## get the positive region:
pos.region = BC.positive.reg.RST(hiring.data, roughset)
pos.region

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