  Many techniques for handling missing data have been proposed in the
literature. Most of these techniques are overly complex. This paper explores an
imputation technique based on rough set computations. In this paper,
characteristic relations are introduced to describe incompletely specified
decision tables.It is shown that the basic rough set idea of lower and upper
approximations for incompletely specified decision tables may be defined in a
variety of different ways. Empirical results obtained using real data are given
and they provide a valuable and promising insight to the problem of missing
data. Missing data were predicted with an accuracy of up to 99%.
