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综合多重评价因素的Web用户聚类算法 被引量：4

An Algorithm of Integrating Multiple Evaluating-Factors for Clustering Web-Users
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摘要 文章提出了综合多重评价因素的Web用户聚类算法;首先从评价因素的数学特征出发,提出了Web资源偏爱度与Web资源关联度的概念,然后运用Kruskal算法的基本原理在由Web资源和Web访问行为所构成的无向图内寻找寻频繁路径,再根据频繁路径和Web资源偏爱度与关联度阈值对Web用户进行聚类处理。该算法在一定程度上提高了Web用户聚类算法的准确性与执行效率。 A new algorithm for Clustering Web-Users with Integrating Evaluating-Factors is proposed in this paper. First,by analyzing the mathematical characteristics of evaluating-factors,this paper proposes the concepts of preference and relation of Web resources.Then,we use the basic principle of Kruskal to search for frequent traversal paths in the undigraph composed of web resources and web-users＇ activities,and cluster Web-Users according to the frequent traversal path and the thresholds of preference and relation.This algorithm improves the accuracy and efficiency of the algorithms for clustering web-users.
作者 吴跃进
机构地区 北京航空航天大学计算机学院
出处 《计算机工程与应用》 CSCD 北大核心 2006年第28期147-149,210,共4页 Computer Engineering and Applications
关键词 评价因素 偏爱度 关联度 频繁路径 用户聚类 evaluating-factor,preference,relation,frequent path,cluster Web-user
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献8

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二级参考文献19

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共引文献124

同被引文献45

引证文献4

二级引证文献5

计算机工程与应用
2006年 第28期

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