基于多最小支持度的多层模糊关联规则挖掘

为了在事务数据库中发现关联规则,在现实挖掘应用中,经常采用不同的标准去判断不同项目的重要性,管理项目之间的分类关系和处理定量数据集这3个方法去处理问题,因此提出一个在定量事务数据库中采用多最小支持度,在项目集中获取隐含知识的多层模糊关联规则挖掘算法。该挖掘算法使用两种支持度约束和至上而下逐步细化的方法推导出频繁项集,同时可以发现交叉层次的模糊关联规则。通过实例证明了该挖掘算法在多最小支持度约束下推导出的多层模糊关联规则是易于理解和有意义的,具有很好的效率和伸缩性。

Based on multiple minimum supports of multi-level fuzzy association rules mining

In order to find association rules in transaction databases of real applicatioins,different criteria is often used to judge the importance of different items,managing taxonomic relationships among items,and dealing quantitative data sets are three issues that are usually dealt with the problem.A multiple-level fuzzy association rules algorithm is proposed for extracting know-ledge implicit in quantitative transactions with multiple minimum supports of items.Using two kinds of support constraints,the proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets.Meanwhile,it can also discover cross-level fuzzy association rules.An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level fuzzy association rules under multiple minimum supports constraint.Multi-layer fuzzy association rules are easy to understand and meaningful,with good efficiency and scalability.