Rough rule extraction of extension group decision-making under incomplete information

In order to improve the accuracy and the reliability of data mining of extension group decision-making by making comparison, selection and identification of objects under incomplete information, this paper studies extension classification, attribution reduction, rule extraction and data forecast of extension group decision-making based on combining extension transformation and group decision optimization. Not only does this method take the advantages of dynamic classification and data mining, but also achieves the promotion of the classification results of multi-factor analysis and multi-project evaluation in extension group decision-making.