Abstract: Association rules is a crucial problem in Knowledge Discovery in Databases（KDD）.Fuzzy association rules can be used to represent human knowledge in terms of natural language，and have recently received much attention from the KDD researcher.So far，however，most approaches of fuzzy association rules discovery are based on the measures of support and confidence for classical association rules.In fact，fuzzy association rules can be interpreted in different way，and the interpretation has a strong influence on the way of finding rules.From the logical point of view，fuzzy logic rules，support degree，implication degree and some related concepts are defined，and the algorithm of fuzzy logic rules discovery is proposed.This algorithm integrates the concepts of fuzzy logic and Apriori algorithm to find fuzzy logic rules from given quantitative databases.

摘要： 关联规则是数据库中的知识发现（KDD）领域的重要研究课题。模糊关联规则可以用自然语言来表达人类知识，近年来受到KDD研究人员的普遍关注。但是，目前大多数模糊关联规则发现方法仍然沿用经典关联规则发现中常用的支持度和置信度测度。事实上，模糊关联规则可以有不同的解释，而且不同的解释对规则发现方法有很大影响。从逻辑的观点出发，定义了模糊逻辑规则、支持度、蕴含度及其相关概念，提出了模糊逻辑规则发现算法，该算法结合了模糊逻辑概念和Apriori算法，从给定的定量数据库中发现模糊逻辑规则。