摘要
传统的关联规则挖掘没有考虑各项目的重要程度,因此实际过程中缺乏一定的针对性.在New-Apriori算法的加权支持度基础上结合Fp-growth算法思想,提出了基于Fp-树的加权关联规则算法,并给出了关联规则的个性化推荐的一般过程.利用Web日志文件采用网页被用户选择的频率作为权重值,实现了个性化推荐系统的算法.实验结果表明该算法具有较高的准确性和效率.
Conventional association rule mining does not consider the importance of each item, so in fact applying it lacks some pertinency. Based on the weighted support of New-Apriori algorithm and combining the Fp-growth algorithm ideas, the weighted association rule algorithm is put forward based on Fp-tree. And the general process of personalization recommendation of the association rule is given. By using Web log file, making use of the Web page frequency which is visited by users as its weight, the algorithm is implemented in the personalization recommendation. The experimental results also show the algorithm has high veracity and efficiency.
出处
《郑州大学学报（理学版）》
CAS
2007年第2期65-69,共5页
Journal of Zhengzhou University:Natural Science Edition
基金
2006年上海市高校优秀青年教师培养基金资助项目
