关键词: 模糊关联规则, 形式化方法, 约束级别（RL）, 一般一元假设自动机（GUHA）模型, 支持度, 置信度, 确定性因子

Abstract: Fuzzy association rules have been used to deal with imprecision in databases and offer a good representation of knowledge discovery. Using restriction level representation theory, GUHA model is generalized for fuzzy association rules. By restriction levels managing fuzzy rules, an extended validation measurement process is presented. Mining algorithm using the formal method, parallels the mining process in different restriction levels, and then summarizes the obtained results. Algorithm complexity analysis and experimental results show that this method is feasible and effective, which sets a logic basis for the representation and evaluation of fuzzy association rules.

Key words: fuzzy association rules, formal method, Restriction Level（RL）, General Unary Hypothesis Automaton（GUHA） model, support, confidence, certainty factor