Doug Beeferman
- 1.R. Agrawal, T. hnielinski and A. Swami (1993). MiDing association rules between sets of items in a Image database. Proceedings of the A CM Conference on Management of Data (SIGMOD'93). Washington, DC. Google Scholar
Digital Library
- 2.D. Boley, M. Gini, R. Gross, S. Han, K. Hastings,G. Km'ypis, V. Kumm', B. Mobasher and J. Moore (1999). Pm'titioning-based clustering for web document categorization. Journal of Decision Support Systems. Google ScholarDigital Library
- 3.S. Brin, R. Motwani and C. Silverstein (1997). Beyond mm'ket baskets: Generalizing association rules to con'elations. Proceedings of the A CM Conference on Management of Data (SIGMOD'97). Tuscon, AZ. Google ScholarDigital Library
- 4.Broder, S. Glassman and M. Manasse (1997). Syntactic clustering of the web. Proceedings of the Sixth International World Wide Web Conference. Santa Clma, CA. Google ScholarDigital Library
- 5.T. Cormen, C. Leiserson and R. Pdvest (1990). Introduction to Algorithms. MIT Press. Google ScholarDigital Library
- 6.J. Heflocker, J. Konstan, A. Borchers and J. Riedl (1999). Am algorithmic fi'amework for performing collaborative filtering. Proceedings of the A CM Conference on Research and Development in Information Retrieval (SIGIR '99). Berkeley, CA. Google ScholarDigital Library
- 7.Inktomi Coorate Web Page (2000). wss. inkt omi. com.Google Scholar
- 8.A. Jain and R. Dubes (1988). Algorithms for clustering data. Prentice Hall. Google ScholarDigital Library
- 9.B. JaJlsen, A. Spin, k, J. Batema and T. Saracevic (1998). Real-life ifformation retrieval: A study of user queries on the Web. SIGIR Forum 32(1), 5 17. Google ScholarDigital Library
- 10.J. Kleinberg (1998). Authoritative sources in a hyperliked enviromnent. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms. Google ScholarDigital Library
- 11.J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon and J. Riedl (1997). GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM 40(3): 787. Google ScholarDigital Library
- 12.G. Millighan and M. Cooper (1985). An exatnination of proee&tres for detecting the number of clusters in a data set. Psychometrika 50, 159 179.Google ScholarCross Ref
- 13.B. Mobasher, N. Jain, E. Han, J. Srivastava (1996). Web Mining: Pattern discovery from Word Wide Web Transactions. Technical Report TR96-050, Depat'tment of Computer Science, University of Mimesota.Google Scholar
- 14.NPD Seatch and Portal Site Survey (2000). Pubfished by NPD New Media Services. Reported in www. searchenginewatch, com.Google Scholar
- 15.G. Salton (1989). Automatic text processing. Addison-Wesley. Google ScholarDigital Library
- 16.N. Slavenat and H. Gatcia-MoUna (1996). Building a sealable and accurate copy detection mechanism. Proceedings of the First ACM Conference on Digital Libraries (DL '96). Bethesda, MD. Google ScholarDigital Library
- 17.C. Silverstein, M. Henzinger, H. Marals and M. Moricz (1998). Analysis of a very large AltaVista query log. DEC SRC Technical Note 1998-1999.Google Scholar
- 18.P. Willet (1988). Recent trends in hierachical document clustering: A critical review. Information Processing and Management 24: 57597. Google ScholarDigital Library
- 19.O. Zatnir and O. Etzioni (1998). Web document clustering: A feasibifity demonstration. Proceedings of the ACM Conference on Information Retrieval (SIGIR'98), Melbourne, Australia. Google ScholarDigital Library
Index Terms
- Agglomerative clustering of a search engine query log
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