Mining rough association from text documents

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4259 LNAI, Issue , 2006, Pages 368-377

  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

DATA ACQUISITION; DATA MINING; DATA REDUCTION; KNOWLEDGE ENGINEERING; LOGIC PROGRAMMING;

ASSOCIATION RULES; DATA VALUES; TEXT DOCUMENTS; WEIGHT DISTRIBUTION;

ROUGH SET THEORY;

EID: 33845277375     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11908029_39     Document Type: Conference Paper

References (24)

1. nd IEEE International Conference on Data Mining, Japan, 2002, 19-26.
2. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison Wesley, 1999.
3. 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.
4. M. Eirinaki and M. Vazirgiannis, Web mining for web personalization, ACM Transactions on Internet Technology, 2003, 3(1): 1-27.
5. D. A. Evans, et al., CLARIT experiments in batch filtering: term selection and threshold optimization in IR and SVM Filters, TREC02, 2002.
6. nd ACM SIGKDD, 1996, 343-346.
7. R. Feldman, et. al., Maximal association rules: a new tool for mining for keyword co-occurrences in document collection, KDD97, 1997, 167-170.
8. R. Feldman, et. al., Text mining at the term level, in Lecture Notes in Artificial Intelligence 1510, Springer, 65-73.
9. R. Feldman, I. Dagen, and H. Hirsh, Mining text using keywords distributions, Journal of Intelligent Information Systems, 1998, 10(3): 281-300.
10. D. A. Grossman and O. Frieder, Information retrieval algorithms and heuristics, Kluwer Academic Publishers, Boston, 1998.
11. rd IEEE International Conference on Data Mining, 2003, Melbourne, Florida, USA, 529-532.
12. J. D. Holt and S. M. Chung, Multipass algorithms for mining association rules in text databases, Knowledge and Information Systems, 2001, 3: 168-183.
13. X. Li and B. Liu, Learning to classify texts using positive and unlabeled data, IJCAI, 2003, 587-592.
14. Y. Li and N. Zhong, Web mining model and its applications on information gathering, Knowledge-Based Systems, 2004, 17: 207-217.
15. Y. Li and N. Zhong, Capturing evolving patterns for ontology-based, IEEE/WIC/ACM International Conference on Web Intelligence, 2004, Beijing, China, 256-263.
16. rd IEEE International Conference on Data Mining, 2003, Melbourne, Florida, USA, 593-596.
17. 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.
18. 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.
19. rd International Conference on Rough Sets and Current Trends in Computing, USA, 2002, 1-9.
20. Z. Pawlak and A. Skowron, Rough sets and Boolean reasoning, to appear in: Information Science, 2006.
21. S. Robertson, and D. A. Hull, The TREC-9 filtering track final report, TREC-9, 2000.
22. F. Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys, 2002, 34(1):1-47.
23. rd IEEE International Conference on Data Mining, 2003, Melbourne, Florida, USA, 347-354.
24. 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, 2004, Beijing, China, 242-248.