Description Objects from the Class Slots Methods Note Author(s) References See Also Examples

A decision table is a set of rules with the same number of conditions and only one decision.

Objects can be created by calls of the form `new("DecisionTable", decisionTable)`

.
A decisionTable is a numeric matrix where each row is a ruleThe matrix last column is the decision of the rules and the remaining columns are rule conditions.

`decisionTable`

:Object of class

`"matrix"`

- checkConsistency
`signature(object = "DecisionTable")`

: ...- computeConsistencyMatrix
`signature(object = "DecisionTable")`

: ...- computeDiscernibilityMatrix
`signature(object = "DecisionTable")`

: ...- findAllReductsFromCore
`signature(object = "DecisionTable")`

: ...- findFirstConditionReduct
`signature(object = "DecisionTable")`

: ...- findSmallestReductFamilyFromCore
`signature(object = "DecisionTable")`

: ...- getCondition
`signature(object = "DecisionTable")`

: ...- getDecision
`signature(object = "DecisionTable")`

: ...- getDecisionTable
`signature(object = "DecisionTable")`

: ...- getRule
`signature(object = "DecisionTable")`

: ...- initialize
`signature(.Object = "DecisionTable")`

: ...`signature(x = "DecisionTable")`

: ...- removeDuplicatedRulesDT
`signature(object = "DecisionTable")`

: ...- show
`signature(object = "DecisionTable")`

: ...- simplifyDecisionTable
`signature(object = "DecisionTable")`

: ...

This is not a complete implementation of Rough Set theory; instead it is just the application of the theory to decision table simplification also known as knowledge reduction.

Alber Sanchez

Pawlak, Zdzislaw 1991 *Rough Sets: Theoretical Aspects of Reasoning About Data* Dordrecht: Kluwer Academic Publishing.

`DiscernibilityMatrix`

`ConditionReduct`

`ValueReduct`

1 2 3 | ```
exampleMatrix1 <- matrix(c(1,0,2,1,1,2,2,0,0,1,0,1,0,2,1,
1,2,1,0,0,2,0,1,1,2,1,1,2,0,1,1,0,0,2,1,2,1,1,2,1),ncol = 5)
dt <- decisionTable(exampleMatrix1)
``` |

```
```

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