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