WAM-Miner: In the search of Web access motifs from historical Web log data

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2005, Pages 421-428

  Zhao, Qiankun ^a   Bhowmick, Sourav S ^a   Gruenwald, Le ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

Dynamic Pattern; Web Access Motif; Web Usage Mining

Indexed keywords

ALGORITHMS; COMPETITIVE INTELLIGENCE; DATA MINING; DATA PROCESSING; INFORMATION TECHNOLOGY; INTELLIGENT AGENTS; KNOWLEDGE ACQUISITION; STATISTICAL METHODS; WORLD WIDE WEB;

INTELLIGENT WEB ADVERTISEMENT; INTELLIGENT WEB CACHING; WAM-MINER ALGORITHM; WEB ACCESS MOTIF (WAM); WEB USAGE MINING TECHNIQUE;

SEARCH ENGINES;

EID: 33745803700     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. http://ita.ee.lbl.gov/. Internet traffic archive.
2. R. Agrawal and R. Srikant. Mining sequential patterns. ICDE, 3-14, 1995.
3. A. Amiri and S. Menon. Efficient scheduling of internet banner advertisements. ACM TOIT, 3(4):334-346, 2003.
4. S. Baron, M. Spiliopoulou, and O. Gnther. Efficient monitoring patterns In data mining environments. ADBIS, 253-265, 2003.
5. th Q 2000. TR AdRelevance, 2001.
6. M.-S. Chen, J. S. Park, and P. S. Yu. Efficient data mining for path traversal patterns. TKDE, 10(2):209-221, 1898.
7. V. Ganti, J. Gehrke, and R. Ramakrishnan. A framework for measuring changes in data characteristics. PODS, 1900.
8. V. Ganti and R. Ramakrishnan. Mining and monitoring evolving data. Handbook of massive data sets, 593-642, 2002.
9. J. Garofalakis, P. Kappos, and O. Mourloukos. Web site optimization using page popularity. IEEE Internet Computing, 3(4):22-20, 1999.
10. S. Gunduz and M. T. Ozsu. A web page prediction model based on click-stream tree representation of user behavior. SIGKDD, 635-540, 2003.
11. X. Huang, A. An, N. Cercone, and G. Promhouse. Discovery of interesting association rules from livelink web log data. ICDM, 763-766, 2002.
12. N. Labroche, N. Monmarche, and G. Venturini. Web sessions clustering with artificial ants colonies. WWW, 2003.
13. T. Li, Q. Yang, and K. Wang:. Classification pruning for web-request prediction. WWW, 2001.
14. B. Liu, W. Hsu, and Y. Ma. Discovering the set of fundamental rule changes. In Proc. of SIGKDD, 335-340, 2001.
15. B. Mobasher, R. Cooley, and J. Srivastava. Creating adaptive web sites through usage-based clustering of URLs. IEEE KDEX Workshop, 1999.
16. J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen, U. Dayal, and M. Hsu. PrefixSpan: mining sequential patterns by prefix-projected growth. ICDE, 215-224, 2001.
17. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-Mine: hyper-structure mining of frequent patterns in large databases. ICDM, 441-448, 2001.
18. J. Pei, J. Han, B. Mortazavi-asl, and H. Zhu. Mining access patterns efficiently from web logs. PAKDD, 396-407, 2000.
19. J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2):12-23, 2000.
20. S. Weisberg. Applied linear regression. Wiley; 2 edition, July, 1985.
21. Y. Xiao and M. H. Dunham. Efficient mining of traversal patterns. DKE, 39(2):191-214, 2001.
22. Y. Xiao, J.-F Yao, Z. Li, and M. K. Dunham. Efficient data mining for maximal frequent subtrees. ICDM, 379-386, 2003.
23. M. J. Zaki. Efficiently mining frequent trees in a forest. SIGKDD, 2002.