【摘要】 针对传统关联规则可视化挖掘方法不利于处理多值属性数据、缺乏展现数据间的频繁模式和关联模式以及效率低下等问题,提出了基于KAF因子和CHF因子的Apriori改进算法进行多值属性关联规则挖掘,实现了一种新的基于概念格的多值属性关联规则可视化方法.运用概念格理论对多值属性数据进行了重新定义和分类,建立了较为完整的挖掘过程参数调整策略,方便用户选择关键属性值进行规则挖掘分析,提高了算法运行速度和挖掘效率.以概念格结构将多值数据组织起来,实现了对频繁项集的可视化展示,以及关联规则的多模式可视化展示.实验结果表明,改进后的挖掘算法具有更好的性能,所提出的可视化形式和已有成果相比具有良好的展现效果.更多还原

【Abstract】 Considering the problems aroused by the traditional association rules visualization mining methods which are lack of dealing with multi-valued attribute data,especially not conducive to expressing the frequent pattern between items and representing multi-schema association rules,this paper,which presents the redefinition and classification of multi-valued attribute data by using conceptual lattice,proposes an improvement of Apriori algorithm based on the KAF factor and the CHF factor to mine multi-valued attribute association rules as well as introduces a novel visualizing approach for multi-valued association rules based on concept lattice,and establishes a complete mining course parameters adjustment strategy acting very well in improving the speed and efficiency of mining algorithm,which is convenient for users to select key attribute values to mine and analyze rules. This methodology organically organizes the multi-valued attribute data with concept lattice structure,which has achieved frequent itemset visualization and multi-schema visualization of association rules. The experimental results turn out that the improved mining algorithm has a better performance and the schema has much excellent visual effects for multi-schema association rules visualization.更多还原