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The rough-set theory proposed by Pawlak, has been widely used in dealing withdata classification problems. The original rough-set model is, however, quitesensitive to noisy data. Tzung thus proposed deals with the problem ofproducing a set of fuzzy certain and fuzzy possible rules from quantitativedata with a predefined tolerance degree of uncertainty and misclassification.This model allowed, which combines the variable precision rough-set model andthe fuzzy set theory, is thus proposed to solve this problem. This paper thusdeals with the problem of producing a set of fuzzy certain and fuzzy possiblerules from incomplete quantitative data with a predefined tolerance degree ofuncertainty and misclassification. A new method, incomplete quantitative datafor rough-set model and the fuzzy set theory, is thus proposed to solve thisproblem. It first transforms each quantitative value into a fuzzy set oflinguistic terms using membership functions and then finding incompletequantitative data with lower and the fuzzy upper approximations. It secondcalculates the fuzzy {\beta}-lower and the fuzzy {\beta}-upper approximations.The certain and possible rules are then generated based on these fuzzyapproximations. These rules can then be used to classify unknown objects.

Learning Fuzzy {\beta}-Certain and {\beta}-Possible rules from incomplete quantitative data by rough sets