A Novel Fuzzy Positive and Negative Association Rules Algorithm

摘要：

According to the existing mining algorithm of fuzzy association rules, a novel fuzzy positive and negative association rules algorithm will be proposed in this paper. We focus on the membership function of fuzzy set and minimum support parameters of positive and negative association rules and adopt a method that selects parameters automatically which is based on the k-means clustering. Besides, multi-level fuzzy support and correlation coefficient are chosen to restrain the quantity and quality of rules generated by the algorithm. Finally the validity and accuracy of the algorithm are proved by an experiment.

关键词： data mining fuzzy association rules membership function multi-level fuzzy support correlation coefficient

作者: HU Kai

作者单位: China ship Development & Design Center Wuhan, China

会议类型: 国际会议

会议名称: 第九届分布式计算及其应用国际学术研讨会

会议地点: 香港

会议语种:英文

页码: 623-628

在线出版日期: 2010-08-12（万方平台首次上网日期，不代表论文的发表时间）