期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

集成Web使用挖掘和内容挖掘的用户浏览兴趣迁移挖掘算法 被引量：5

Combining Web Usage and Content Mining for User Navigation Interest Conversion Patterns
在线阅读 下载PDF
导出
摘要 提出了一种集成 Web使用挖掘和内容挖掘的用户浏览兴趣迁移模式的模型和算法 .介绍了 Web页面及其聚类 .通过替代用户事务中的页面为相应聚类的方法得到用户浏览兴趣序列 .从用户浏览兴趣序列中得到用户浏览兴趣迁移模式 .该模型对于网络管理者理解用户的行为特征和安排 Web站点结构有较大的意义 . We present a model for discovering user navigation interest conversion patterns that combine Web Usage Mining and Web Content Mining. Introduce the vector representation of Web page and page clustering, then get user navigation interest sequences by replacing the page of user sessions with its corresponding cluster number. user navigation interest conversion patterns could be mined from the user navigation interest sequences. At the end of this paper, experiments on a web site are conducted. Our model is very useful for web managers to understand user behavior and arrange Web site structure in reason.
作者 邢东山 沈钧毅 宋擒豹
机构地区 西安交通大学电子与信息工程学院
出处 《小型微型计算机系统》 CSCD 北大核心 2004年第7期1170-1173,共4页 Journal of Chinese Computer Systems
基金 国家自然科学基金 (60 173 0 5 8)资助
关键词 浏览兴趣 迁移模式 WEB内容挖掘 WEB使用挖掘 电子商务 navigation interest conversion pattern Web content mining Web usage mining E-business
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献13

  • 1[1]Mobasher B, Jain N and Han E and Srivastava J. Web Mining: Pattern discovery from world wide Web transactions[R]. Technical Report TR96-050, Department of Computer Science, University of Minnesota, 1996.
  • 2[2]Spiliopoulou M. The laborious way from data mining to Web mining[J]. Int. Journal of Comp. Sys., Sci. & Eng., Special Issue on Semantics of the Web, 1999,14:113-126.
  • 3[3]Büchner A G, Mulvenna M D. Discovering internet marketing intelligence through online analytical Web usage mining[J]. ACM SIGMOD Record, ISSN 0163-5808, 1998, 27(4):54-61.
  • 4[4]Madria S, Bhowmick S, Ng W K, Lim E P. Research issues in Web data mining[C]. DAWAK99, Florance, Italy. Proc. Springer-verlag as LNCS, 1999.
  • 5[5]Kowalski G. Information retrieval systems-theory and implementation[M]. Kluwer Academic Publishers, 1997.
  • 6[6]Larsen B, Aone C. Fast and effective text mining using linear-time document clustering[C]. KDD-99, San Diego, California, 1999.
  • 7宋擒豹,沈钧毅.基于关联规则的Web文档聚类算法[J].软件学报,2002,13(3):417-423. 被引量：41
  • 8[8]Shahabi C, Zarkesh A, Adibi J, Shah V. Knowledge discovery from users web-page navigation[C]. In:Proceedings of the IEEE RIDE97 Workshop, April 1997.
  • 9[9]Büchner A G, Mulvenna M D. Discovering behavioural patterns in internet log files: playing the devil's advocate[C]. 12th Biennial Intl Telecommunications Society Conf. (ITS-98), Stockholm, Sweden, 1998.
  • 10[10]Chen M S, Park J S, Yu P S. Efficient data mining for path traversal patterns[J]. IEEE Trans. on Knowledge and Data Engineering, 1998, 10(2):209-221.

二级参考文献7

  • 1[1]Broder,A.Z.,Glassman,S.C.,Manasse,M.S.Syntactic clustering of the Web.Technical Report,1997-015,Palo Alto,CA:Digital Systems Research Center (Digital),1997.
  • 2[2]Chang,C.H.,Hsu,C.C.Customizable multi-engine search tool with clustering.Computer Network and ISDN Systems,1997,29(8-13):1217~1224.
  • 3[3]Chen,L.,Katya,S.Webmate:a personal agent browsing and searching.In:Sycara,K.P.,Wooldridge,M.,eds.Proceedings of the 2nd International Conference on Autonomous Agents.New York:ACM Press,1998.132~139.
  • 4[4]Ron,W.,Bienvenido,V.,Mark,A.S.,et al.Hypursuit:a hierarchical network search engine that exploits content-link hypertext clustering.In:ACM,ed.Proceedings of the 7th ACM Conference on Hypertext.New York:ACM Press,1996.180~193.
  • 5[5]Ackerman,M.,Billsus,D.,Gaffney,S.,et al.Learning probabilistic user profiles.AI Magazine,1997,18(2):47~56.
  • 6[6]Cheeseman,P.,Stutz,J.Bayesian classification (autoclass):theory and results.In:Fayyad,U.M.,Piatetsky-Shapiro,G.,Smyth,P.,et al.,eds.Advances in Knowledge Discovery and Data Mining.Menlo Park,CA:AAAI/MIT Press,1996.153~180.
  • 7[7]Agrawal,R.,Srikant,R.Fast algorithm for mining association rules.In:Jorge,B.B,Matthias,J.,Carlo,Z.,eds.Proceedings of the 20th International Conference on Very Large Databases.Santiago:Morgan Kaufmann Publishers,Inc.,1994.487~499.

共引文献40

同被引文献21

引证文献5

二级引证文献8

小型微型计算机系统
2004年 第7期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
关于我们 | 版权声明 | 联系我们 | 帮助中心| 意见反馈
主办单位：上海市教育委员会
技术支持：重庆维普资讯有限公司
渝公网安备 50019002500403号
使用帮助 返回顶部