加权模糊关联挖掘算法

Algorithm of weight fuzzy association rules

针对数量型关联规则挖掘中划分边界过硬问题,以及加权关联规则中为确保向下封闭性成立而引起的规则丢失问题,提出一种新的加权模糊关联挖掘模型及其挖掘算法NFWARM。为了避免区间划分引起的边界过硬问题,该模型引入模糊集软化属性的划分边界;同时,使用属性权重刻画元素对规则的贡献,在保证频繁项集向下封闭性的情况下,不会引起规则丢失。实验结果表明,该算法适用于包含布尔型和数值型数据的大型数据库的规则挖掘,并且得到的频繁项目集数目和规则数目有显著增加。

To solve the problem of strong division in mining quantitative association rules and rules loss caused by ensuring the validation of downward closure property in weighted association rules,a new weighted fuzzy association rules mining model and its mining algorithm NFWARM are proposed.In the model,boundaries of division are softened using fuzzy set,and the problem of strong division is avoided;in the meantime,elements’ contribution to rules is measured by attribute weight,and the problem of rules loss is avoided with the validation of downward closure property ensured.The experimental results show that the proposed algorithm applies to the rule mining of large database including boolean and numerical data,and the number of the obtained frequent item sets and rules have significantly increased.