This function implements a fundamental part of RST: the indiscernibility relation. This binary relation indicates whether it is possible to discriminate any given pair of objects from an information system.
1  BC.IND.relation.RST(decision.table, feature.set = NULL)

decision.table 
an object inheriting from the 
feature.set 
an integer vector indicating indexes of attributes which should be used or an object inheriting from
the 
This function can be used as a basic building block for development of other RSTbased methods.
A more detailed explanation of the notion of indiscernibility relation can be found in A.IntroductionRoughSets
.
An object of a class "IndiscernibilityRelation"
which is a list with the following components:
IND.relation
: a list of indiscernibility classes in the data. Each class is represented by indices
of data instances which belong to that class
type.relation
: a character vector representing a type of relation used in computations. Currently,
only "equivalence"
is provided.
type.model
: a character vector identifying the type of model which is used.
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.LU.approximation.RST
, FS.reduct.computation
, FS.feature.subset.computation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  #############################################
data(RoughSetData)
hiring.data < RoughSetData$hiring.dt
## In this case, we only consider the second and third attribute:
A < c(2,3)
## We can also compute a decision reduct:
B < FS.reduct.computation(hiring.data)
## Compute the indiscernibility classes:
IND.A < BC.IND.relation.RST(hiring.data, feature.set = A)
IND.A
IND.B < BC.IND.relation.RST(hiring.data, feature.set = B)
IND.B

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