基于结构化属性集的规则学习

Rule learning algorithm based on structured attribute set

提出了从结构化属性的背景中学习关联规则的通用算法,该算法使用过滤函数检查频繁概念,只需修改该函数,就可得到各种基于概念的规则。该算法的优点是在计算过程中利用属性结构化消除频繁概念中的冗余内涵,使得到的规则更精炼、更实用。

An algorithm with a filter function which exploring frequent concepts in a context was proposed with structured attributes in order to learn association rules.Simply changing the function that algorithm could compute various kinds of concept-based rules.The advantage of the presented method is to avoid the redundancy in the intent of the computed frequent concepts with the help of structured attributes.The resulted rules are therefore more concise and practical.