摘要
讨论了加权关联规则的挖掘算法 .对布尔型属性 ,在挖掘算法 MINWAL ( O)和 MINWAL ( W)的基础上给出一种改进的加权关联规则挖掘算法 ,此算法能有效地考虑布尔型属性的重要性和规则中所含属性的个数 .对数量型属性 ,应用竞争聚集算法将数量型属性划分成若干个模糊集 ,并系统地提出加权模糊关联规则的挖掘算法 .此算法能有效地考虑数量型属性的重要性和规则中所含属性的个数 。
Algorithms for mining the weighted association rules are discussed in this paper. As far as Boolean attributes are concerned, an improved algorithm for mining the weighted association rules is provided based on the mining algorithms of MINWAL(O) and MINWAL(W). This algorithm can effectively consider the importance of Boolean attributes and the amount of attributes in the rule. As for quantitative attributes, they are divided into several fuzzy sets by the competitive agglomeration algorithm, and then the algorithm for mining weighted fuzzy association rules is provided. This algorithm can effectively consider the importance of quantitative attributes and the amount of attributes in the rule, and can be fit for large database.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2002年第10期1281-1286,共6页
Journal of Computer Research and Development
基金
国家自然科学基金重点项目资助(69931040)
关键词
数据挖掘
关联规则
模糊集
支持率
可信度
竞争聚集算法
数据库
data mining, association rules, fuzzy sets, support, confidence, competitive agglomeration algorithm
