A scalable algorithm for high-quality clustering of Web snippets

Proceedings of the ACM Symposium on Applied Computing

Volumn 2, Issue , 2006, Pages 1058-1062

  Geraci, Filippo ^a,b   Pellegrini, Marco ^a   Pisati, Paolo ^a   Sebastiani, Fabrizio ^c  

^a Istituto di informatica e telematica del Consiglio nazionale delle ricerche   (Italy)

^b UNIVERSITY OF SIENA   (Italy)

^c UNIVERSITY OF PADOVA   (Italy)

Author keywords

Clustering; Meta search engines; Metric spaces; Web snippets

Indexed keywords

COMPUTATIONAL METHODS; METADATA; PROBLEM SOLVING; SEARCH ENGINES; WORLD WIDE WEB;

CLUSTERING; METRIC SPACES; WEB SNIPPETS;

ALGORITHMS;

EID: 33750377487     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1141277.1141527     Document Type: Conference Paper

References (17)

1. D. Cheng, R. Kannan, S. Vempala, and G. Wang. On a recursive spectral algorithm for clustering from pairwise similarities. Technical Report MIT-LCS-TR-906, Massachusetts Institute of Technology, Cambridge, US, 2003.
2. E. Di Giacomo, W. Didimo, L. Grilli, and G. Liotta. A topology-driven approach to the design of Web meta-search clustering engines. In Proceedings of SOFSEM-05, 31st Annual Conference on Current Trends in Theory and Practice of Informatics, Liptovský Ján, SK, 2005.
3. T. Feder and D. Greene. Optimal algorithms for approximate clustering. In Proceedings of STOC-88, 20th ACM Symposium on Theory of Computing, pages 434-444, Chicago, US, 1988.
4. P. Ferragina and A. Gulli. A personalized search engine based on Web-snippet hierarchical clustering. In Special Interest Tracks and Poster Proceedings of WWW-05, International Conference on the World Wide Web, pages 801-810, 2005.
5. T. F. Gonzalez. Clustering to minimize the maximum intercluster distance. Theoretical Computer Science, 38(2/3):293-306, 1985.
6. M. A. Hearst and J. O. Pedersen. Reexamining the cluster hypothesis: Scatter/Gather on retrieval results. In Proceedings of SIGIR-96, 19th ACM International Conference on Research and Development in Information Retrieval, pages 76-84, Zürich, CH, 1996.
7. Y. Maarek, R. Fagin, I. Ben-Shaul, and D. Pelleg. Ephemeral document clustering for Web applications. Technical Report RJ 10186, IBM, San Jose, US, 2000.
8. J. MacQueen. Some methods-for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281-297, 1967.
9. S. Osinski and D. Weiss. Conceptual clustering using Lingo algorithm: Evaluation on Open Directory Project data. In Proceedings of HPWM-04, 5th Conference on Intelligent Information Processing and Web Mining, pages 369-377, Zakopane, PL, 2004.
10. J. M. Peña, J. A. Lozano, and P. Larrañaga. An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters, 20(10):1027-1040, 1999.
11. S. J. Phillips. Acceleration of k-means and related clustering algorithms. In Proceedings of ALENEX-02, 4th International Workshop on Algorithm Engineering and Experiments, pages 166-177, San Francisco, US, 2002.
12. S. Selim and M. Ismail. K-means type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(1):81-87, 1984.
13. M. Steinbach, G. Karypis, and V. Kumar. A comparison of document clustering techniques. In Proceedings of the ACM KDD-00 Workshop on Text Mining, Boston, US, 2000.
14. A. Strehl, J. Ghosh, and R. J. Mooney. Impact of similarity measures on Web-page clustering. In Proceedings of the AAAI Workshop on AI for Web Search, pages 58-64, Austin, US, 2000.
15. O. Zamir and O. Etzioni. Web document clustering: A feasibility demonstration. In Proceedings of SIGIR-98, 21st ACM International Conference on Research and Development in Information Retrieval, pages 46-54, Melbourne, AU, 1998.
16. P. Zezula, G. Amato, V. Donnal, and M. Batko. Similarity search: The metric space approach. Springer-Verlag, Heidelberg, DE, 2006. Forthcoming.
17. D. Zhang and Y. Dong. Semantic, hierarchical, online clustering of Web search results. In Proceedings of APWEB-04, 6th Asia-Pacific Web Conference, pages 69-78, Hangzhou, CN, 2004.