Mining incomplete data - A rough set approach

Many real-life data sets are incomplete. or in different words. are affected by missing attribute values. Three interpretations of missing attribute values arc discussed in the paper: lost values ( erased values). attribute-concept values(such a value may he replaced by any Value from the attribute domain restricted to the concept) , and "do not care" conditions (a missing attribute value may he replaced by any value from the attribute domain). For incomplete data sets three definitions of lower and upper approximations ire discussed. Experiments were conducted oil three different interpretations of missing attribute values six typical data set,, with missing attribute values. Using and the same definition of concept lower and upper approximations. The conclusion is that the best approach to missing attribute values is the lost value type.