摘要
实际中收集的数据类型具有多样性,如何从这些复杂数据中获取有用的知识是人们进行数据挖掘的目标。由于覆盖粗糙集可以处理复杂的数据,基于此对覆盖决策系统的属性约简和规则提取已有不少的研究。已有的覆盖决策系统规则提取的研究只考虑唯一的置信度评估度量,然而提取的高置信度规则覆盖的样例可能较少而具有欺骗性,由此本文又引入了一个评估规则覆盖能力的度量,从而可以消除数据中的偶然因素,获取泛化能力强的高置信度规则。在此基础上,为了提取紧凑的规则,给出了一个规则置信度保持的属性约简启发式算法。
The data collected in practice is of diversity. Howto obtain useful knowledge from the complex data is the objective of data mining. Since covering rough sets can deal with complex data,there exists much study on the attribute reduction and rule acquisition of covering decision systems based on covering rough sets. The existing research on the rule acquisition of covering decision systems considered the confidence measure as the only evaluation criterion. However,the extracted high-confidence rules may cover fewer instances and then be potentially spurious. Therefore,a measure that can assess the coverage ability of rules is introduced,which can eliminate the chance in data and thus acquire high-confidence rules with more generalization ability. Furthermore,in order to extract compact rules,we propose a rule confidence-preserved attribute reduction heuristic algorithm.
作者
张晓
杨燕燕
ZHANG Xiao;YANG Yan-yan(School of Sciences,Xi,an University of Technology,Xi'an 710048,Shaanxi,China;Department of Automation,Tsinghua University,Beijing 100084,China)
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2018年第12期120-126,共7页
Journal of Shandong University(Natural Science)
基金
国家自然科学基金资助项目(61602372)
西安理工大学博士研究启动基金(109-256081504)
关键词
粗糙集
覆盖决策系统
规则提取
属性约简
rough sets
covering decision systems
rule acquisition
attribute reduction
