R: Computation of indiscernibility classes based on the rough...

BC.IND.relation.RST {RoughSets} | R Documentation

## Computation of indiscernibility classes based on the rough set theory

### Description

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.

This function can be used as a basic building block for development of other RST-based methods. A more detailed explanation of the notion of indiscernibility relation can be found in `A.Introduction-RoughSets`.

### Usage

    BC.IND.relation.RST(decision.table, feature.set = NULL)

### Arguments

`decision.table` | an object inheriting from the `"DecisionTable"` class, which represents a decision system. See `SF.asDecisionTable`.
`feature.set` | an integer vector indicating indexes of attributes which should be used or an object inheriting from the `FeatureSubset` class. The computed indiscernibility classes will be relative to this attribute set. The default value is `NULL` which means that all conditional attributes should be considered. It is usually reasonable to discretize numeric attributes before the computation of indiscernibility classes.

### Value

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.

### Author(s)

Andrzej Janusz

### References

Z. Pawlak, "Rough Sets", International Journal of Computer and Information Sciences, vol. 11, no. 5, p. 341 - 356 (1982).

### See Also

`BC.LU.approximation.RST`, `FS.reduct.computation`, `FS.feature.subset.computation`

### Examples

    #############################################
    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

[Package RoughSets version 1.3-7 Index]