摘要
关联规则的挖掘是知识发现的重要研究内容之一．目前对关联规则的研究，仅限于用确定的、精确的概念表示的确定关联规则．由于客观世界的多样性和复杂性，许多事物难于用精确和确定的概念表示，用确定关联规则不能有效地表达数据之间的关联规则．本文提出了模糊关联规则的概念，研究了模糊关联规则的性质和挖掘算法，同时还提出了一种新的规则有趣性的度量函数．
Mining association rules is one of the important aspects of knowledge discovery. In current researches, mining definite association rules which is expressed by the definite and accurate concept, has been studied. In many circumstances, the objective data is complex and diversity, is difficult to be expressed by definite and accurate concept. Fuzzy associations rules expressed by the fuzzy concept is proposed in the paper. An algorithm for mining fuzzy association rules is also proposed. A new interestingness function which can measure the novelty of rules is given.
出处
《小型微型计算机系统》
CSCD
北大核心
1999年第4期270-274,共5页
Journal of Chinese Computer Systems
基金
国家自然科学基金
