Clustering web queries

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2009, Pages 899-908

  Whissell, John S ^a   Clarke, Charles L A ^a   Ashkan, Azin ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Clustering; Clustering evaluation; Query intent detection

Indexed keywords

CLUSTERING EVALUATION; CLUSTERING METHODS; CLUSTERING QUALITY; CLUSTERING WEB; CLUSTERINGS; DATA SETS; INTENT DETECTION; LABELINGS; MULTIPLE CLUSTERINGS; SEARCH ENGINE RESULTS; WEB SEARCH ENGINES; WEIGHTING SCHEME;

CLASSIFIERS; INFORMATION RETRIEVAL; KNOWLEDGE MANAGEMENT; POPULATION STATISTICS; SEARCH ENGINES; WEB SERVICES;

CLUSTERING ALGORITHMS;

EID: 74549208545     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1645953.1646069     Document Type: Conference Paper

References (35)

1. A. Ashkan, C. L. A. Clarke, E. Agichtein, and Q. Guo. Classifying and Characterizing Query Intent. In Proceedings of the 31st European Conference on IR, pages 578-586, 2009.
2. R. Baeza-Yates. Applications of Web Query Mining. In Lecture Notes in Computer Science: Advances in Information Retrieval, volume 3408, pages 7-22. Springer Berlin, 2005.
3. R. Baeza-Yates, L. Calderán-Benavides, and C. González- Caro. The intention behind web queries. In Lecture Notes in Computer Science: SPIRE, volume 4209, pages 98-109. Springer Berlin, 2006.
4. D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. In Proceedings of the sixth ACM SIGKDD international conference on Knolwedge dicovery and data mining, pages 407-416, 2000.
5. J. C. Bezdek. Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press.
6. A. Broder. A taxomony of web search. ACM SIGIR Forum, 36:3-10, 2002.
7. R. M. Castro, M. J. Coates, and R. D. Nowak. Likelihood based heirarchical clustering. IEEE Transactions on Signal Processing, 52:2308-2321.
8. R. Caruana, M. Elhawary, N. Nguyen, and C. Smith. Meta Clustering. In Proceedings of the Sixth International Conference on Data Mining, 2006.
9. H. Dai, L. Zhao, Z. Nie, J. Wen, L. Wang, and Y. Li. Detecting online commercial intention (OCI). In 15th International Conference on the World Wide Web, pages 829-837, 2006.
10. A. Dempster, N. Laird, and D. Rubin. Likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society of Britian, B, 39:1-38, 1977.
11. C. Ding, X. He, H. Zha, M. Gu, and H. Simon. Spectral min-max cut for graph partitioning and data clustering. Technical Report TR-2001-XX, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA., 2001.
12. D. Gondek and T. Hofmann. Conditional information bottleneck clustering. In 3rd IEEE International Conference on Data Mining, Workshop on Clustering Large Data Sets, pages 36-42, 2003.
13. G. Hamerly and C. Elkan. Learning the k in k-means. In Neural Information Processing Systems, 2003.
14. T. Hofmann. Probabilistic latent semantic analysis. In Uncertainty in Artificial Intelligence, pages 289-296, 1999.
15. T. Hofmann. Probabilistic latent semantic indexing. In ACM SIGIR 1999, 1999.
16. J. Hu, L. Fang, Y. Cao, H. Zeng, H. Li, Q. Yang, Z. Chen. Enhancing text clustering by leveraging Wikipedia semantics. In ACM SIGIR 2008, pages 179-186, 2008.
17. M. Hosseini and H. Abolhassani. Hierarchical Co-clustering for Web Queries and Selected URLs.
18. A. K. Jain, M. N. Murthy, and P. J. Flynn. Data clustering: A review. ACM Computing Reviews, pages 264-323, 1999.
19. R. E. Kass and L. Wasserman. A reference Bayesian test for nested hypothesis and its relationship to the Schwarz criterion. Journal of the American Statistical Association, 90(431):928-934, 1995.
20. T. Lange, M.L. Braun, V. Rother, and J.M. Buhmann. Stability-Based Model Selection. In Advances in Neural Information Processing Systems 15, 2003.
21. U. Lee, Z. Liu, and J. Cho. Automatic identification of user goals in Web search. In Proceedings of the 14th International World Wide Web Conference, pages 391-400, Chiba, Japan, 2005.
22. S. Lloyd. Least squares quantization in PCM. IEEE Transactions on Information Theory, 28:129-137, 1982.
23. D. Nettleton, L. Calderán-Benavides, and R. Baeza-Yates. Analysis of web search engine query session and clicked documents. In Advances in Web Mining and Web Usage Analysis, pages 207-226, 2007.
24. D. Pelleg and A. Moore. X-means: Extending k-means with an efficient estimation of the number of clusters. In Proceedings of the 17th International Conference on Machine Learning, pages 727-734, 2000.
25. D. E. Rose and D. Levinson. Understanding user goals in web search. In Proceedings of 13th International World Wide Web Conference, pages 13-19, 2004.
26. S. Ben-David, U. von Luxburg, and D. Pal. A Sober Look at Stability of Clustering. COLT, 2006.
27. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 2000.
28. N. Slonim and N. Tishby. Document clustering using word clusters via the information bottleneck method. In ACM SIGIR 2000, pages 208-215, 2000.
29. R. R. Snokal and P. H. Sneath. Numerical Taxonomy, pages 230-234. W. H. Freeman and Company, San Francisco, 1973.
30. R. Tibshirani, G. Walter, and T. Hastie. Estimating the number of data clusters via the gap statistic. Journal of the Royal Statistical Society, Series B 63, 2001.
31. L. Xu, J. Neufeld, B. Larson, and D. Schuurmans. Maximum margin clustering. In Advances in Neural Information Processing Systems 17, pages 1537-1544, 2005.
32. W. Xu, X. Liu, Y. Gong. Document clustering based on non-negative matrix factorization. In ACM SIGIR 2003, pages 267-273, 2003.
33. M. Yan and K. Ye. Determining the Number of Clusters Using the Weighted Gap Statistic. Biometrics 63.
34. B. Zhang, M. Hsu, and U. Dayal. Generalized k-harmonic means - boosting in unsupervised learning. Technical Report HPL-1999-124, Hewlett-Packard Labs, 1999.
35. Y. Zhao and G. Karypis. Evaluation of hierarchical clustering algorithms for document datasets. In Data Mining and Knowledge Discovery, 2002.