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基于全置信度关联分析的web层次聚类方法 被引量：2

Hierarchical clustering Web pages method based on all-confidence association analysis
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摘要 为了便于用户浏览网页信息,基于全置信度关联分析,提出了一种网页层次聚类的方法。该方法采用向量空间模型表示网页文档,将文档看成事务,文档的词汇视为事务中的项,根据关联挖掘算法发现文档之间的强关联规则产生基本类,然后利用图划分的算法完成网页文档的层次聚类。在关联规则产生过程中采用全置信度量发现强关联模式,规则的产生不受支持度阈值设置的影响,即使支持度阈值设置为零,也能发现强关联模式,有效地消除了弱相关的交叉支持模式。 In order to facilitate users to browse web pages, an algorithm based on all-confidence association analysis is proposed. In this algorithm, Vector Space Model （VSM） is employed to represent web documents, in which web documents are represented as transactions and words in the web documents are considered as items of the transactions. According to the strong affinity association rules produced by association mining algorithms, base clusters are generated, and finally web pages are grouped in a hierarchical fashion by using graph partition method. During the process of association rules generation, all-confidence is used to discover strong affinity pattern, by which cross-support patterns are efficiently avoided and the support threshold has little influence to the association rules even if the threshold is set to zero.
作者 史庆伟 赵政 鲍虎
机构地区 天津大学计算机科学与技术学院
出处 《辽宁工程技术大学学报（自然科学版）》 EI CAS 北大核心 2007年第6期892-894,共3页 Journal of Liaoning Technical University (Natural Science)
基金 天津市科技发展计划基金资助项目(07JCZDJC067007)
关键词 关联规则 层次聚类 网页文档 文本挖掘 association rules hierarchical clustering Web documents text mining
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
作者简介 史庆伟（1973-），男，辽宁阜新人，讲师，博士研究生，主要研究方向为Web挖掘，信息抽取
  • 相关文献

参考文献7

  • 1史庆伟,赵政,朝柯.一种基于后缀树的中文网页层次聚类方法[J].辽宁工程技术大学学报（自然科学版）,2006,25(6):890-892. 被引量：11
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二级参考文献14

  • 1郭伟,唐晓君,刘万军.一种基于划分的聚类算法分析与改进[J].辽宁工程技术大学学报（自然科学版）,2004,23(6):826-828. 被引量：4
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共引文献50

同被引文献27

  • 1刘川,方思行.基于FPclose算法挖掘强亲密度关联模式[J].计算机工程与设计,2005,26(5):1149-1151. 被引量：1
  • 2马建庆,钟亦平,张世永.基于兴趣度的关联规则挖掘算法[J].计算机工程,2006,32(17):121-122. 被引量：20
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  • 10ZHANG J, Feigenbaum J. Finding highly correlated pairs efficiently with powerful pruning [C]// Proceedings of the 10th ACM SIGKDD International Conference on Information and Knowledge Management. New York. ACM, 2006: 152-161.

引证文献2

二级引证文献1

辽宁工程技术大学学报（自然科学版）
2007年 第6期

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