【摘要】 关联规则是数据依赖关系的有效描述方法，是知识发现研究的重要内容．传统的关联规则挖掘算法缺少挖掘的针对性，挖掘速度慢，挖掘结果难于理解，挖掘结果的数量巨大，需要进行大量的筛选以便抽取出有用规则．文中提出了将概念融入挖掘过程中，提高挖掘的效率和挖掘的针对性的方法，给出了概念指导的关联规则挖掘算法 Ｃ Ｇ Ａ Ｒ Ｍ 和大数据库中概念的交互式生成方法．算法 Ｃ Ｇ Ａ Ｒ Ｍ 是对基于分类的挖掘算法的拓展．实验结果表明，算法 Ｃ Ｇ Ａ Ｒ Ｍ 提高了挖掘结果的有趣性，挖掘速率比传统的多层次关联规则挖掘算法 Ｃｕｍ ｕｌａｔｅ 快一倍更多还原

【Abstract】 Association rules is an effective method for describing the dependency relations in data, and it is one of the improtant aspects of knowledge discovery. Taditional association rule mining methods lack of focus on the results, and the procedure is slow. Those algorithms express the regularities with low level primitive data, and the mining association reules are difficult to understand. Furthermore, the desirable knowledge must be filtered out from huge results in a post\|processing step. A method for integrating concepts into the mining procedures to improve the interestingness of results and speeding up mining procedures is proposed, and the method for deriving concepts interactively in large database is proposed, a concept\|guided association rules mining algorithm CGARM is given in the paper here. CGARM extends taxonomies\|based mining methods. Experiments show the execution speed of CGARM is about twice as faster as the traditional mining algorithm Cumulate, and the interestingness of results are also improved.更多还原