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The theory of soft set proposed by Molodtsov in 1999 is a new method for handling uncertain data and can be redefined as a Boolean-valued information system. The soft set theory has been applied to data analysis and decision support systems based on large data sets. Using retrieved datasets, the authors will be comparing two techniques in solving incomplete datasets : parity bits of supported set and the aggregate and calculated support values. It is demonstrated in this paper that the technique using aggregate values and calculated support values performs better in the process of identifying missing values in incomplete datasets.
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