Session boundary detection for association rule learning using n-gram language models

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

Volumn 2671, Issue , 2003, Pages 237-251

  Huang, Xiangji ^a   Peng, Fuchun ^a   An, Aijun ^b   Schuurmans, Dale ^a   Cercone, Nick ^c  

^a UNIVERSITY OF WATERLOO   (Canada)

^b YORK UNIVERSITY   (Canada)

^c DALHOUSIE UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ASSOCIATION RULES; IMAGE SEGMENTATION; INFORMATION THEORY; COMPUTER SCIENCE; FORMAL LANGUAGES; INFORMATION TECHNOLOGY; MATHEMATICAL MODELS; STATISTICAL METHODS;

BOUNDARY DETECTION; DYNAMIC SESSIONS; FIXED TIME; INFORMATION-THEORETIC APPROACH; INTERESTINGNESS MEASURES; LOG DATA; N-GRAM LANGUAGE MODELS; SESSION IDENTIFICATION;

COMPUTATIONAL LINGUISTICS; LEARNING SYSTEMS;

AD HOC TIMEOUT METHOD; ASSOCIATION RULE LEARNING; N-GRAM LANGUAGE MODELS; SESSION BOUNDARY DETECTION;

EID: 7044253000     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44886-1_19     Document Type: Conference Paper

References (13)

1. Agrawal, R. and Srikant, R.; (1994). Fast Algorithms for Mining Association Rules, Proc. of the 20th International Conference on Very Large Databases, Santiago, Chile.
2. An, A. and Cercone, N.; (2001). Rule Quality Measures for Rule Induction Systems: Description and Evaluation, Computational Intelligence, Vol. 17 No. 3.
3. Bahl, L., Jelinek, F. and Mercer, R.; (1983). A Maximum Likelihood Approach to Continuous Speech Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(2), pp. 179-190.
4. Bruha, I.; (1996). Quality of Decision Rules: Definitions and Classification Schemes for Multiple Rules. In Nakhaeizadeh, G. and Taylor, C. C. (eds.): Machine Learning and Statistics, The Interface. Jone Wiley & Sons Inc.
5. Chen, S. and Goodman, J.; (1998). An Empirical Study of Smoothing Techniques for Language Modeling. Technical report, TR-10-98, Harvard University.
6. Catledge, Lara D. and Pitkow, James E.; (1995) Characterizing Browsing Strategies in the World Wide Web, Proceedings of the 3rd International World Wide Web Conference, April 1995, Darmstadt, Germany.
7. He, D. and Goker, A.; (2000). Detecting session boundaries from Web user logs, Proceedings of the 22nd Annual Colloquium on Information Retrieval Research (ECIR), April 2000, Sidney Sussex College, Cambridge, England.
8. Hiemstra, D.; (2001). Using Language Models for Information Retrieval. Ph.D. Thesis, Centre for Telematics and Information Technology, University of Twente.
9. Huang, X., An, A., Cercone, N. and Promhouse, G; (2002) Discovery of Interesting Association Rules from Livelink Web Log Data. In Proceedings of the IEEE International Conference on Data Mining (ICDM), December, 2002, Maebashi TERRSA, Maebashi City, Japan.
10. Lafferty, J. and Zhai, C.; (2001). Document Language Models, Query Models, and Risk Minimization for Information Retrieval. In Proceedings of 24th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).
11. Peng, F. and Schuurmans, D.; (2003). Combining Naive Bayes and n-Gram Language Models for Text Classification. In Proceedings of The 25th European Conference on Information Retrieval Research (ECIR).
12. Ponte, J. and Croft, W.; (1998). A Language Modeling Approach to Information Retrieval. In Proceedings of ACM Research and Development in Information Retrieval (SIGIR), 275-281.
13. Tan, P. and Kumar, V.; (2000). Interestingness Measures for Association Patterns: A Perspective, Technical Report TR00-036, Department of Computer Science, Univ. of Minnestota.