Testing the cluster hypothesis in distributed information retrieval

Information Processing and Management

Volumn 42, Issue 5, 2006, Pages 1137-1150

  Crestani, Fabio ^a   Wu, Shengli ^b  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^b UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

Clustering; Distributed information retrieval; Experimental study; Information retrieval; Retrieval results presentation

Indexed keywords

EXPERIMENTS; HIERARCHICAL SYSTEMS; MERGING; QUERY LANGUAGES; RESEARCH;

CLUSTER HYPOTHESIS; CLUSTERING; DISTRIBUTED INFORMATION RETRIEVAL; DOCUMENT BROWSING; EXPERIMENTAL STUDY; RETRIEVAL RESULTS PRESENTATION;

INFORMATION RETRIEVAL;

EID: 33646028483     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2005.12.002     Document Type: Article

References (33)

1. Callan, J. K., Lu, Z., & Croft, W. (1995). Searching distributed collections with inference networks. In Proceedings of the 18th annual international ACM SIGIR conference, Seattle, USA (pp. 21-28).
2. Calve A.L., and Savoy J. Database merging strategy based on logistic regression. Information Processing and Management 36 3 (2000) 341-359
3. Croft W.B. A model of cluster searching based on classification. Information Systems 5 3 (1980) 189-195
4. Dhillon I.S., Fan J., and Guan Y. Efficient clustering of very large document collections. Data mining for scientific and engineering applications (2001), Kluwer Academic Publishers
5. Dreilinger D., and Howe A. Experiences with selecting search engines using metasearch. ACM Transactions on Information Systems 15 3 (1997) 195-222
6. Everitt B. Cluster analysis. 3rd ed. (1993), Edward Arnold, London, UK
7. Fuhr N. A decision-theoretic approach to database selection in networked IR. ACM Transactions on Information Systems 17 3 (1999) 229-249
8. Gauch S., Wang G., and Gomez M. Profusion: Intelligent fusion from multiple distributed search engines. Journal of Universal Computer Science 2 9 (1996) 637-649
9. Gravano L., García-Molina H., and Tomasic A. Gloss: text-source discovery over the internet. ACM Transactions on Database Systems 24 2 (1999) 229-264
10. Griffiths A., Robinson A., and Willett L.A. Hierarchical agglomerative clustering methods for automatic document classification. Journal of Documentation 40 3 (1984) 175-205
11. Hawking D., and Thistlewaite P. Methods for information server selection. ACM Transactions on Information Systems 17 1 (1999) 40-76
12. Hearst, M. A., & Pedersen, J. O. (1996). Reexamining the cluster hypothesis: Scatter/gather on retrieval results. In Proceedings of the 19th annual international ACM SIGIR conference, ZTH, Zürich, Switzerland (pp. 76-84).
13. Jardin N., and van Rijsbergen C.J. The use of hierarchical clustering in information retrieval. Information Storage and Retrieval 7 5 (1971) 217-240
14. Lawrence S., and Giles C.L. Searching the world wide web. Science 280 3 (1998) 98-100
15. Lee, J. H. (1997). Analysis of multiple evidence combination. In Proceedings of the 20th annual international ACM SIGIR conference, Philadelphia, PA, USA (pp. 267-275).
16. Leuski, A. (2001). Evaluating document clustering for interactive information retrieval. In Proceedings of ACM international conference on knowledge management, Atlanta, GA, USA (pp. 33-40).
17. Meng, W., Liu, K., Yu, C., Wang, X., Chang, Y., & Rishe, N. (1998). Determining text databases to search in the internet. In Proceedings of the 24th international conference on very large data bases, New York City, USA (pp. 14-25).
18. Ogilvie, P., & Callan, J. (2001). Experiments using the lemur toolkit. In Proceedings of the 2001 text retrieval conference, Gaithersburg, MD, USA (pp. 103-108).
19. Salton G. Full text information processing using the smart system. Data Engineering Bulletin 13 1 (1990) 2-9
20. Shaw, J. A., & Fox, E. A. (1995). In Proceedings of 3rd text retrieval conference (trec-3), Gaithersburg, MD, USA.
21. Si, L., & Callan, J. (2002). Using sampled data and regression to merge search engine results. In Proceedings of the 25th annual international ACM SIGIR conference, Tempere, Finland (pp. 19-26).
22. Spath H. Cluster analysis algorithms for data reduction and classification of objects (1980), Ellis Horwood Limited
23. Sumner, R. G., Jr., & Shaw, W. M., Jr. (1996). An investigation of relevance feedback using adaptive linear and probabilistic models, Gaithersburg, MD, USA, November 20-22.
24. Tombros A., Villa R., and van Rijsbergen C.J. The effectiveness of query-specific hierarchic clustering in information retrieval. Information Processing and Management 38 4 (2002) 559-582
25. Tsikrika, T., & Lalmas, M. (2001). Merging techniques for performing data fusion on the web. In Proceedings of the 2001 ACM CIKM international conference on information and knowledge management, Atlanta, GA, USA (pp. 127-134).
26. Voorhees, E. M. (1985). The effectiveness and efficiency of agglomerative hierarchical clustering in document retrieval (Tech. Rep. No. TR 85-705). Ph.D. Thesis, Department of Computer Science, Cornell University, Ithaca, NW, USA.
27. Voorhees, E. M., Gupta, N. K., & Johnson-Laird, B. (1995). Learning collection fusion strategies. In Proceedings of the 18th annual international ACM SIGIR conference, Seattle, WA, USA (pp. 172-179).
28. Voorhees, E. M., & Harman, D. K. (Eds.). (1996). Proceedings of the 5th text retrieval conference, Gaithersburg, MD, USA.
29. Vort C.C., and Cotterell G.A. A fusion via a linear combination of scores. Information Retrieval 1 3 (1999) 151-173
30. Willett P. Recent trends in hierarchical document clustering: A critical review. Information Processing and Management 24 5 (1988) 557-597
31. Wu S., and Crestani F. Distributed information retrieval: A multiobjective resource selection approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11 (2003) 83-100
32. Xu, J., & Croft, W. B. (1999). Cluster-based language models for distributed retrieval. In Proceedings of the 21st annual international ACM SIGIR conference, Berkeley, CA, USA (pp. 254-261).
33. Yuwono, B., & Lee, D. (1997). Server ranking for distributed test retrieval systems on the internet. In Proceedings of the fifth international conference on database systems for advanced application, Melbourne, Australia (pp. 41-50).