摘要
为了表示复杂庞大的概念层次树,文中提出了一种更加通用的编码方案,将概念分层应用于模糊关联规则的挖掘.此外,为解决隶属度函数难以主观确定的问题,引入一种SOFM网络来确定样本数据的隶属度函数.基于改进的概念层次树的编码方案和SOFM网络,将模糊集引入关联规则挖掘中,设计了一种新的多层模糊关联规则挖掘算法.实验结果表明,该算法可以有效地挖掘出易于理解的、有意义的多层次模糊关联规则,具有很好的效率和伸缩性.
In order to represent a complex large concept hierarchy tree, this paper proposes a more general coding scheme which applies concept hierarchy into the mining of fuzzy association rules. As it is difficult to determine the membership function subjectively, a self-organizing feature map （SOFM） network is introduced to determine the membership function of sample data. Based on the improved coding scheme and the SOFM network, fuzzy set is then introduced to design a new algorithm of mining multi-level fuzzy association rules. Experimental results show that the proposed algorithm is of high efficiency and scalability and can effectively mine multi-level fuzzy association rules that are meaningful and easily understandable.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第5期81-85,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(60273043)
安徽省自然科学基金资助项目(050420204)
安徽省教育厅自然科学研究项目(KJ2007B153)
关键词
自组织特征映射网络
概念分层
模糊集
关联规则
self-organizing feature map network
concept hierarchy
fuzzy set
association rule
