Attribute reduction and decision rule generation based on rough sets

In this paper, we present an application of rough set methods for rule generation, which includes two steps. Attribute reduction is done first, which reduces the number of unnecessary attributes for the whole decision table. In this process, the relative subdivision distance is proposed as the heuristic information of algorithm REDA. The completeness and the correctness of REDA are also proven from the theoretical analysis. Value reduction is performed afterwards, which reduces the number of redundant attributes for every object. In this process, properties of attribute values of three classes are analyzed and the maximum number of condition attribute values is removed without losing essential information according to their properties. We then can get the optimal decision rules. Experimental results from applying the rough set approach to the set of data samples are given and evaluated to show efficient and effective of our algorithm.