个性化决策规则的发现: 一种基于Rough Set 的方法

为发现用户真正感兴趣的决策规则，利用RS理论和方法设计了个性化决策规则发掘算法．算法分为两步：首先在属性约简中通过提出的理论尽可能去除用户不感兴趣的属性的方法来找出最佳约简；然后在属性值约简中进一步去除与用户无关的属性，从而抽取个性化决策规则．从理论上论证了算法的有效性，给出了实验分析，证实了算法的可行性．

Discovery of personalized decision rule:A rough-set-based approach

Based on rough set theory and method, an algorithm for discovering personalized decision rules is designed in order to find the rules which users really want. The expounded algorithm consists of two steps. First, the attributes with less interest of user are eliminated as possible during reduction of attributes, and in this way the best reduct can be found. Secondly, during reduction of attribute values, some irrelevant attributes are further eliminated, then abstractation of personalized decision rules is accomplished. The algorithm is proved efficient in theory, and the results of experiment show the feasibility of this algorithm.