摘要
针对决策表中有隐含属性层次的决策表,讨论了属性层次树,定义出属性层次决策表,提出一个算法来对属性层次决策表进行规则挖掘,并通过实例分析了算法的有效性。实验结果表明,高属性层次决策表的对象数明显少于原决策表对象数。在对象数明显减少的高属性层次决策表上的约简运算量比直接在原决策表上的要减小很多。
Based on the attributes hierarchy decision table, the attributes hierarchy trees were introduced and the con-cept of attributes hierarchy decision table was defined. An algorithm was proposed for the rules mining in the attributes hierarchy decision table and numerical examples were employed to substantiate this algorithm. And the experiment indi- cates that the number of objects in attributes hierarchy decision table is less than original decision table, and the time of implementing attributes reduction is less than that.
出处
《计算机科学》
CSCD
北大核心
2013年第10期198-202,共5页
Computer Science
基金
国家自然科学基金项目(60970061
61075056
61103067)
中央高校基本科研业务费专项资金资助
关键词
属性层次树
属性层次决策表
粗糙集
属性约简
Attributes hierarchy tree, Attribute hierarchy decision table, Rough sets, Attributes reduction
作者简介
杨伟 博士生，主要研究方向为模式识别与智能系统、粗糙集理论、粒计算，E-mail：yangw312@yahoo．com．cn；
苗夺谦 教授，博士生导师，主要研究方向为人工智能、模式识别、知识发现、粗糙集理论等。
