Learning fuzzy rules from incomplete quantitative data by rough sets

In this paper, we deal with the problem of learning from incomplete quantitative data sets based on rough sets. Quantitative values are first transformed into fuzzy sets of linguistic terms using membership functions. Unknown attribute values are then assumed to be any possible linguistic terms and are gradually refined according to the fuzzy incomplete lower and upper approximations derived from the given quantitative training examples. The examples and the approximations then interact on each other to derive certain and possible rules and to estimate appropriate unknown values. The rules derived can then serve as knowledge concerning the incomplete quantitative data set.