摘要
模糊关联规则可以用自然语言来表达人类知识,受到数据挖掘与知识发现研究人员的广泛关注。但是,目前大多数模糊关联规则挖掘方法仍然基于经典关联规则的支持度和可信度测度。从模糊蕴涵的观点出发,定义了模糊Horn子句规则、支持度、蕴涵强度以及相关概念,提出了模糊Horn子句规则挖掘算法。该算法可以分解为3个步骤。首先,将定量数据库转换为模糊数据库。其次,挖掘模糊数据库中所有支持度不小于指定最小支持度阈值的频繁项目集。一旦得到了所有频繁项目集,就可以用一种直接的方法生成所有蕴涵强度不小于指定最小蕴涵强度阈值的模糊Horn子句规则。
Fuzzy association rules can be used to represent human knowledge in terms of natural language,and has attracted a growing amount of attention from the communities of Data Mining and Knowledge Discovery.However,so far,most approaches of mining fuzzy association rules are based on the measures of support and confidence for classical association rules.From the viewpoint of fuzzy implications,fuzzy Horn clause rules,degree of support,implication strength and some related concepts were defined,and an algorithm was proposed for mining fuzzy Horn clause rules.This algorithm can be decomposed into three subprocess.First of all,a quantitative database is transformed into a fuzzy database.Secondly,all frequent itemsets in the fuzzy database that are contained in a sufficient number of transactions above the minimum support threshold are identified.Once all frequent itemsets are obtained,the desired fuzzy Horn clause rules above the minimum implication strength threshold can be generated in a straightforward manner.
出处
《计算机科学》
CSCD
北大核心
2011年第9期142-145,共4页
Computer Science
关键词
模糊关联规则
模糊Horn子句规则
支持度
蕴涵强度
定量数据库
模糊数据库
Fuzzy association rules
Fuzzy horn clause rules
Degree of support
Implication strength
Quantitative databases
Fuzzy databases
