【摘要】 提出了一种基于 Rough有限概念格的规则发现方法 (RRLIRD) ,以揭示数据集中的蕴涵规则 .首先引入有限概念格的简化表示形式 ,由用户选择数据集中感兴趣属性集创建概念格结构 ,提高用户的交互性和挖掘的效率 ;然后运用有限概念格与 Rough集理论相结合形成 Rough有限概念格 ,蕴涵规则则由其特有的上、下近似运算得到 ,不需计算繁琐的频繁项目集 .算法运用大型超市的交易流水数据进行仿真实验 .结果表明 ,执行时间比经典的 Apriori算法大大降低 .该算法也适用于证券行情分析和农业数据库中的病虫害分析等 .更多还原

【Abstract】 An efficient algorithm was found to discover the implication rules in a data set. As an important data mining technique, the implication rules can help to explore the dependencies among values of attributes of a database. The algorithm first extends the concept lattice theory by building the simplified lattice structure according to the data set with the resticted attributes to improve human interaction and mining efficiency. The constrained concept lattice, together with the rough set theory, is then incorporated into the method to implement a new restricted rough lattice based implication rules discovery (RRLIRD) approach to interactively acquire the rules with the specific rough upper and lower approximation. The algorithm is different from the classical rule extraction methods without computing the frequent itemsets. For the application to the transaction data set of large scale supermarkets, a simulation was implemented to demonstrate that the approach can reduce the computational time greatly comparing with that of the Apriori algorithm. The algorithm can also be extended to other areas such as stock analysis and agricultural application.更多还原