Abstract
In the majority of papers on rough set theory itis assumed that the information is complete, i.e., that forall cases all attribute values and decision values arespecified. Such a decision table is said to be completelyspecified.In practice, however, input data, presented as decisiontables, may have missing attribute and decision values,i.e., decision tables are incompletely specified. In this paper we use a variation relation describing the decision table with missing attribute values, i.e. replacing all the missing attribute values by minimum error sums of square for the total variation and thereby completing the information table. Subsequently, we find the reduct and core of the complete decision table and verify that the reduct and core by our method is better than ones by ROSE2 software. Thereafter we generate the rules based on reduct. The most important thing is to be different in the decision rules according to handling missing attribute values.
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