Association rule-based decision making in table data

We have been coping with several aspects of rough sets in non–deterministic information systems (NISs) and tables with inexact data. We are simply calling this work rough non–deterministic information analysis (RNIA). As for decision making in RNIA, we at first obtain rules in tables, then we apply them to the current condition. Therefore, there may be a case that the current condition may not match with the condition part in the obtained rules. In order to recover this unmatched case, this paper newly considers direct question–answering in tables, and proposes association rule–based decision making in tables. The validity of the decision is defined by the criterion values of the association rule. This decision making recovers the complementary functionality in the application of obtained rules. Some examples of execution by the implemented software tool are also presented.