基于粒计算高效挖掘决策型关系数据库中关联规则

文章提出了一种基于粒计算从决策型关系数据集中快速提取关联规则方法,按照属性利用等价类对实体进行分类,利用分类后的属性值来构建粒,提出了基于粒计算提取决策型关系数据库的关联规则算法,来提取关系数据集的关联规则,通过实例来验证该方法的有效性,最后给出了性能分析,并指出基于关系数据集上的粒计算在提取关联规则方面的不足。

MINING ASSOCIATIVE RULES EFFECTIVELY IN DECISION-MAKING RELATION DATABASE BASED ON GRANULAR COMPUTING

The paper has put forward a method based on granular computing to effectively obtain association rules in decision-making relation dataset.Based on granular computing,the method constructs models for the relation dataset,utilizes equivalence class to classify the entity based on attribute so as to construct granules,and brings forward the algorithm based on granular computing to obtain associative rules in decision-making relation dataset.It has been proved effective by empirical study,which yields performance analysis as well as points out the shortcomings of granular computing based on relation dataset in terms of association rules mining.