Rough association rule mining in text documents for acquiring Web user information needs

Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), WI'06

Volumn , Issue , 2006, Pages 226-232

  Li, Yuefeng ^a   Zhong, Ning ^b  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b MAEBASHI INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; INFORMATION RETRIEVAL; ROUGH SET THEORY; WEB SERVICES;

DATA VALUES; WEB INFORMATION GATHERING;

DATA MINING;

EID: 42549146935     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WI.2006.151     Document Type: Conference Paper

References (23)

1. nd IEEE International Conference on Data Mining, Japan, 2002, 19-26.
2. G. Chang, M.J. Healey, J. A. M. McHugh, and J. T. L. Wang, Mining the World Wide Web: An information search approach, Kluwer Academic Publishers, 2001.
3. M. Eirinaki and M. Vazirgiannis, Web mining for web personalization, ACM Transactions on Internet Technology, 2003, 3(1): 1-27.
4. D. A. Evans, et al., CLARIT experiments in batch filtering: term selection and threshold optimization in IR and SVM Filters, TREC02, 2002.
5. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthrusamy, eds., Advances in knowledge discovery and data mining, Menlo Park, California: AAAI Press/ The MIT Press, 1996.
6. nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1996, 343-346.
7. rd IEEE International Conference on Data Mining, USA, 2003, 529-532.
8. J. D. Holt and S. M. Chung, Multipass algorithms for mining association rules in text databases, Knowledge and Information Systems, 2001, 3: 168-183.
9. X. Li and B. Liu, Learning to classify texts using positive and unlabeled data, IJCAI, 2003, 587-592.
10. Y. Li and N. Zhong, Web mining model and its applications on information gathering, Knowledge-Based Systems, 2004, 17:207-217.
11. Y. Li and N. Zhong, Capturing evolving patterns for ontology-based, IEEE/WIC/ACM International Conference on Web Intelligence, Beijing, China, 2004, 256-263.
12. rd IEEE International Conference on Data Mining, USA, 2003, 593-596.
13. Y. Li and N. Zhong, Mining ontology for automatically acquiring Web user information needs, IEEE Transactions on Knowledge and Data Engineering, 2006, 18(4): 554-568.
14. rd IEEE International Conference on Data Mining, USA, 2003, 179-186.
15. J. Mostafa, W. Lam and M. Palakal, A multilevel approach to intelligent information filtering: model, system, and evaluation, ACM Transactions on Information Systems, 1997, 15(4): 368-399.
16. rd International Conference on Rough Sets and Current Trends in Computing, USA, 2002, 1-9.
17. F. Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys, 2002, 34(1): 1-47.
18. rd IEEE International Conference on Data Mining, USA, 2003, 347-354.
19. S.-T. Wu, Y. Li, Y. Xu, B. Pham and P. Chen, Automatic pattern taxonomy exatraction for Web mining, IEEE/WIC/ACM International Conference on Web Intelligence, Beijing, China, 2004, 242-248.
20. H. Yu, J. Han, and K. Chang, PEBL: positive example based learning for Web page classification using SVM, KDD02, 2002, 239-248.
21. J. Han, Y. Fu, Mining Multiple-level Association Rules in Large Databases, IEEE Transactions on Knowledge and Data Engineering, 1999, 11(5): 798-805.
22. G. I. Webb and S. Zhang, K-optimal rule discovery, Data Mining and Knowledge Discovery, 2004, 10: 39-79.
23. th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, China, 2003, 165-172.