A Linear algebra measure of cluster quality

Journal of the American Society for Information Science and Technology

Volumn 51, Issue 7, 2000, Pages 602-613

  Mather, Laura A ^a  

^a Britanicacom Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0012376790     PISSN: 15322882     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1097-4571(2000)51:7<602::AID-ASI3>3.0.CO;2-1     Document Type: Article

References (30)

1. Berry, M.W., Hendrickson, B., & Powell, M.J.D. (1996). Sparse Matrix Reordering Schemes for Browsing Hypertext (Vol. 32, pp. 99-123). American Mathematical Society.
2. Deerwester, S., Dumais, ST., Furnas, G.W., Landauer, T.K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41, 391-407.
3. Dubes, R., & Jain, A.K. (1979). Validity studies in clustering methodologies (Vol. 11, pp. 235-254). UK: Pergamon Press.
4. Dumais, S.T. (1991). Improving the retrieval of information from external sources. Behavior Research Methods. Instruments & Computers, 23, 229-236.
5. El-Hamdouchi, A., & Willett, P. (1987). Techniques for the measurement of clustering tendency in document retrieval systems. Journal of Information Science, 13, 361-365.
6. Fiedler, M. (1973). Algebraic connectivity of graphs. Czechoslovak Mathematical Journal, 23.
7. Golub, G.H., & Van Loan, C.F. (1989). Matrix computations. Baltimore: The Johns Hopkins University Press.
8. Griffiths, A., Robinson, L.A., & Willett, P. (1984). Hierarchical agglomerative clustering methods for automatic document classification. Journal of Documentation, 40, 175-205.
9. Hearst, M.A., & Pederson, J.O. (1996). Reexamining the cluster hypothesis: Scatter/gather on retrieval results. In Proceedings of the ACM SIGIR '96.
10. Hull, D. (1994). Improving text retrieval for the routing problem using latent semantic indexing. In Proceedings of the 17th Annual International SIGIR, ACM-SIGIR.
11. Jardine, N., & van Rijsbergen, C.J. (1971). The use of hierarchical clustering in information retrieval. Information Storage and Retrieval, 7, 217-240.
12. Kolda, T.G., & O'Leary, D.P. (1998). A semi-discrete matrix decomposition for latent semantic indexing in information retrieval. ACM Transactions on Information Systems.
13. Lawson, C.L., & Hanson, R.J. (1974). Solving least squares problems (Chap. 5). NJ: Prentice-Hall.
14. Ling, R.F., & Killough, G.G. (1976). Probability tables for cluster analysis based on a theory of random graphs. Journal of the American Statistical Association, 71, 293-300.
15. Marron, C., & McCloskey, J. (1997). Optimal partitions and clustering. In Proceedings of the 1997 Conference on Linear Algebra and Applications. SIAM.
16. Mather, L.A. (1999). Using singular values to measure cluster quality in information retrieval. In Proceedings of the 1999 SIAM Annual Meeting. SIAM.
17. Montgomery, D.C., & Peck, E. (1992). Introduction to linear regression analysis. New York: John Wiley & Sons.
18. Rasmussen, E.M., & Willett, P. (1989). Efficiency of hierarchic agglomerative clustering using the ICL distributed array processor. Journal of Documentation, 45, 1-24.
19. Rehder, B., Littman, M.L., Dumais, S., & Landauer, T.K. (1997). Automatic 3-language cross-language information retrieval with latent semantic indexing. In Proceedings of 6th Text RE-trieval Conference (TREC-6). National Institute of Standards and Technology (NIST). NIST Special Publication, 1997.
20. Ruocco, A., & Frieder, O. (1997). Clustering and classification of large document bases in a parallel environment. Journal of the American Society for Information Science, 48, 932-943.
21. Schütze, H., & Silverstein, C. (1997). Projections for efficient document clustering. In Proceedings of the 20th Annual International SIGIR (pp. 74-81). ACM-SIGIR.
22. Shaw, Jr., W.M. (1994). Retrieval expectations, cluster-based effectiveness, and performance standards in the CF database. Information Processing and Management, 30, 711-723.
23. Shaw, Jr., W.M., Burgin, R., & Howell, P. (1996). Performance standards and evaluations, in IR test collections: Cluster-based retrieval models. Information Processing and Management, 33, 1-14.
24. Topic Detection and Tracking Workshop. Notes, October 1997.
25. van Rijsbergen, C.J. (1979). Information retrieval. London: Butterworths.
26. van Rijsbergen, C.J. (1979). Information retrieval (Chap. 3). London: Butterworths.
27. Voorhees, E.M. (1985). The cluster hypothesis revisited. Technology Report 85-658, Cornell University, pp. 1-11.
28. Voorhees, E.M. (1986). Implementing agglomerative hierarchical, clustering algorithms for use in document retrieval. Information Processing and Management, 22, 465-476.
29. Walpole, R.E., & Myers, R.H. (1993). Probability and statistics for engineers and scientists. New York: MacMillan Publishing Company.
30. Willett, P. (1989). Recent trends in hierarchical document clustering: A critical review. Information Processing and Management, 24, 577-597.