计算机应用研究2018，Vol.35Issue(1)：93-96,4.DOI:10.3969/j.issn.1001-3695.2018.01.018
基于多重特征的双层Web用户聚类方法
Two-layer Web user cluster based on multiple characteristics
摘要
Abstract
Web log clustering is a general task of exploratory data mining to find user group patterns on the Website and even predict user access behavior.It supplies personalized services for different user groups further.This paper introduced a twolayer Web user clustering method based on multiple feature selection to solve common problems that current algorithms had,including little user interests bias revealed by single feature selection,low convergence efficiency and the diversity of user access impact on traditional hierarchical clusters.This method took use of multiple characteristics for user similarity measurement,and conducted the two-layer clustering on the basis of this metric.First,the method eliminated outliers and detected irregular clusters by DBSCAN clustering.The second layer used the previous clusters to produce the final clusters by a bottomup hierarchical algorithm.Experiments confirm that this method is stable and effective.关键词
Web日志/多重特征/聚类方法/用户相似性度量Key words
Web log/multiple characteristics/clustering method/user similarity measurement分类
信息技术与安全科学引用本文复制引用
王钊,樊钊..基于多重特征的双层Web用户聚类方法[J].计算机应用研究,2018,35(1):93-96,4.基金项目
国家自然科学基金资助项目(61273297) （61273297）
