Mass data mining based on Rough Set and AHP

This paper establishes a reasonable evaluation model with the decision-making attribute as the upper factor and the condition attribute as the lower factor, considering both the advantage of reduction in processing massive data and the advantage of AHP in decision-making. AHP has been used in reduction for the rules extracted. The attribute reduction algorithm utilized to improve the reduce of the magnanimous data redundancy. With the aid of the attribute importance in Rough Set theory, it compensates for the AHP evaluation factor subjective factors. The algorithm model omittes the process of extracting nuclear, analyses the rules extracted quantitatively, and accomplishes the reduction of the massive data attributes and rules. Effectiveness of the algorithm has been proved by examples.