Abstract: After analyzing the association rule mining algorithm of classical concept lattice, in order to solve the issue of mining uncertain rule, this paper puts forward the model of mining interval association rule with parameters, combining with the properties of concept and structure of interval concept lattice. Firstly, this paper gives a series of definitions and related theorems in the course of mining interval rule, and based on the uncertainty of concept lattice, defines the measurable standard of interval association rule. Then, this paper constructs the model of mining rule with parameters based on interval concept lattice, and the analysis shows that the model can effectively extract association rules with high degree of support and confidence, so it improves the dependability of rules. Finally, through a case of book recommendation, this paper verifies the feasibility of this model, meanwhile studies the influence of α and β to interval association rule.

Key words: interval concept lattice, rule mining with parameters, support degree, confidence degree, rule accuracy

摘要： 通过研究基于经典概念格的关联规则提取算法，结合区间概念格的概念性质和结构特性，提出了一种带参数的区间关联规则提取模型，以解决不确定规则的挖掘问题。首先给出了区间规则挖掘过程中的一系列定义和相关定理，并基于区间概念的不确定性，定义了区间关联规则的度量标准——精度和不确定度；之后构建了基于区间概念格的带参数规则挖掘模型，分析表明模型能提取具有较高支持度和置信度的关联规则，提高了规则的可靠性；最后用图书推荐的实例验证了模型的可行性，同时研究了区间参数α和β对区间关联规则的影响。

关键词: 区间概念格, 带参数规则挖掘, 支持度, 置信度, 规则精度