Building association-rule based sequential classifiers for web-document prediction

Data Mining and Knowledge Discovery

Volumn 8, Issue 3, 2004, Pages 253-273

  Yang, Qiang ^a,b   Li, Tianyi ^a   Wang, Ke ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Presending web documents; Sequential classifiers; Web log mining

Indexed keywords

ALGORITHMS; DATA MINING; MATHEMATICAL MODELS; NETWORK PROTOCOLS; ONLINE SEARCHING; STATISTICAL METHODS; WEBSITES; WORLD WIDE WEB;

PRESENDING WEB DOCUMENTS; SEQUENTIAL CLASSIFIERS; WEB LOG MINING;

LOGIC PROGRAMMING;

EID: 3543084503     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:DAMI.0000023675.04946.f1     Document Type: Article

References (14)

1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A.I. 1996. Fast Discovery of Association Rules. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, pp. 307-328.
2. Agrawal, R. and Srikant, R. 1994. Fast algorithm for mining association rules. In Proceedings of the Twentieth International Conference on Very Large Databases, pp. 487-499.
3. Agrawal, R. and Srikant, R. 1995. Mining sequential patterns. In Proceedings of the 1995 Int. Conf. Data Engineering, Taipei, Taiwan, pp. 3-14.
4. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. 1984. Classification and Regression Trees. Wadsworth, Belmont, CA.
5. Liu, B., Hsu, W., and Ma, Y. 1998. Integrating classification and association mining. In Proc. of the Fourth Int'l Conf. on Knowledge Discovery and Data Mining (KDD-98), New York, pp. 80-86.
6. Mannila, H., Toivonen, H., and Verkamo, I. 1998. Discovering frequent episodes in sequences. In Proceedings of the First Int'l Conference on Knowledge Discovery and Data Mining (KDD'95), Montreal, Canada, AAAI Press, pp. 210-215.
7. Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal U., and Hsu, M.-C. 2001. PrefixSpan: Mining sequential patterns efficiently by PrefixProjected pattern growth. In. Proc. 2001 Int. Conf. Data Engineering (ICDE'01), Heidelberg, Germany, pp. 215-224.
8. Pitkow, J. and Pirolli, P. 1999. Mining longest repeating subsequences to predict World Wide Web surfing. In Second USENIX Symposium on Internet Technologies and Systems, Boulder, CO.
9. Quinlan, J.R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann.
10. Srivastava, J., Cooley, R., Deshpande, M., and Tan, P. 2000. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2): 12-23.
11. Schechter, S., Krishnan, M., and Smith, M.D. 1998. Using path profiles to predict HTTP requests. In Proc. 7th International World Wide Web Conference, Brisbane, Qld., Australia, pp. 457-467.
12. Su, Z., Yang, Q., Lu, Y., and Zhang, H. 2000. Whatnext: A prediction system for web requests using N-gram sequence models. In Proc. of the First Int'l Conf. on Web Information Systems and Engineering Conference, Hong Kong, pp. 200-207.
13. Su, Z., Yang, Q., and Zhang, H. 2000. A prediction system for multimedia pre-fetching in Internet. In Proc. 2000 Int'l ACM Conf. on Multimedia, Los Angeles, California.
14. Wang, K., Zhou, S.Q., and He, Yu. 2000. Growing decision trees on association rules. In Proceedings of the 2000 International Conference of Knowledge Discovery in Databases, SIGKDD, pp. 265-269.