The Research on Hierarchical Risk Element Transmission Theory Based on Fuzzy Theory and Data Mining

The traditional analytic hierarchy process (AHP) models are usually based on the precise data and it is difficult to handle the uncertainty evaluation problems. Currently, the studies of AHP models usually evaluate the uncertainty problems based on fuzzy or interval number theory but the evaluation results can not provide complete decision information to the decision makers satisfactorily. Hierarchical risk element transmission (HRET) theory is a new idea of the uncertain evaluation problems. This paper constructs a frame of HRET, at first, fuzzy extended analytic hierarchy process (FEAHP) is used to decide the weight of HRET model, then the triangle fuzzy numbers which is used to solve HRET problems is acquired by the data mining technology based on history data. By doing this, the concluded results can provide more complete information to the decision makers with the consideration of the risk.