Factor matrix text filtering and clustering

Journal of the American Society for Information Science and Technology

Volumn 56, Issue 9, 2005, Pages 946-968

  Kostoff, Ronald N ^a   Block, Joel A ^b  

^a OFFICE OF NAVAL RESEARCH   (United States)

^b RUSH MEDICAL COLLEGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING QUALITY; MATRIX FILTERING; TAXONOMIES; TEXT DATABASES;

ALGORITHMS; COMPETITION; DATA MINING; DATABASE SYSTEMS; MATRIX ALGEBRA; WORD PROCESSING;

TEXT PROCESSING;

EID: 23044445828     PISSN: 15322882     EISSN: None     Source Type: Journal    
DOI: 10.1002/asi.20187     Document Type: Review

References (81)

1. Bookstein, A., Klein, S.T., & Raita, T. (1995). Detecting content-bearing words by serial clustering. In E.A. Fox, P. Ingwersen, & R. Fidel (Eds.), Proceedings of the 18th ACM-SIGIR Conference (pp. 319-327). New York: ACM.
2. Bookstein, A., Klein, S.T., & Raita, T. (1998). Clumping properties of content-bearing words. Journal of the American Society for Information Science, 49(2), 102-114.
3. Bookstein, A., Kulyukin, V., Raita, T., & Nicholson, J. (2003). Adapting measures of clumping strength to assess term-term similarity. Journal of the American Society for Information Science and Technology, 54(7), 611-620.
4. Brill, E. (1993, June). Automatic grammar induction and parsing free text: A transformation-based approach. Paper presented at the 31st Meeting of the Association of Computational Linguistics, Columbus, OH.
5. Brill, E. (1994, June). Some advances in transformation-based part of speech tagging. Paper presented at the Twelfth National Conference on Artificial Intelligence, Seattle, WA.
6. Browne, M.W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research. 36(1), 111-150.
7. Casillas, A., de Lena, M.T.G., & Martinez, R. (2003). Document clustering into an unknown number of clusters using a genetic algorithm. Text, speech and dialogue. Lecture Notes in Artificial Intelligence, 2807, 43-49.
8. Cattell, R.B. (1966). The Scree Test for the number of factors. Multivariate Behavioral Research, 1, 245-276.
9. Cooper, J.C.B. (1983). Factor-analysis-An overview. American Statistician, 37(2), 141-147.
10. Coovert, M.D., & McNelis, K. (1988). Determining the number of common factors in factor-analysis - A review and program. Educational and Psychological Measurement, 48(3), 687-692.
11. Cutting, D.R., Karger, D.R, Pedersen, J.O., & Tukey, J.W. (1992a). Scatter/gather: A cluster-based approach to browsing large document collections. In N.J. Belkin, P. Ingwersen, & A.M. Peitersin (Eds.), Proceedings of the 15th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'92) (pp. 318-329). New York: ACM.
12. Cutting, D., Kupiec, J., Pederson, J., & Sibun, P. (1992b, March-April). A practical part of speech tagger. Paper presented at the Third International ACL Conference on Applied Natural Language Processing, Trento, Italy.
13. Davidse, R.J., & Van Raan, A.F.J. (1997). Out of particles: Impact of CERN, DESY, and SLAC research to fields other than physics. Scientometrics, 40(2), 171-193.
14. Feldman, R., Fresko, M., Kinar, Y., Lindell, Y., Liphstat, O., Rajman, M., et al. (1998). Text mining at the term level. Principles of data mining and knowledge discovery. Lecture Notes in Artificial Intelligence, 1510, 65-73.
15. Feldman, R. (1999). Text mining via information extraction. Principles of data mining and knowledge discovery. Lecture Notes in Artificial Intelligence, 1704, 165-173.
16. Garfield, E. (1985). History of citation indexes for chemistry - A brief review. Journal of Chemical Information and Computer Sciences, 25(3), 170-174.
17. Goldman, J.A., Chu, W.W., Parker, D.S., & Goldman, R.M. (1999). Term domain distribution analysis: A data mining tool for text databases. Methods of Information in Medicine, 38, 96-101.
18. Gordon, M.D., & Dumais, S. (1998). Using latent semantic indexing for literature based discovery. Journal of the American Society for Information Science, 49(8), 674-685.
19. Greengrass, E. (1997). Information retrieval: An overview (Publication No. TR-R52-02-96). Washington, DC: National Security Agency.
20. Guerrero-Bote, V.P., Lopez-Pujalte, C., de Moya-Anegon, F., & Herrero-Solana, V. (2003). Comparison of neural models for document clustering. International Journal of Approximate Reasoning, 34(2-3), 287-305.
21. Guha, S., Rastogi, R., & Shim, K. (1998). CURE: An efficient clustering algorithm for large databases. In L.M. Haas & A. Tiwary (Eds.), Proceedings of the ACM-SIGMOD 1998 International Conference on Management of Data (SIGMOD'98) (pp.73-84). New York: ACM.
22. Hearst, M.A. (1998). The use of categories and clusters in information access interfaces. In T. Strzalkowski (Ed.), Natural language information retrieval. Dordrecht/New York: Kluwer.
23. Hearst, M.A. (1999, June). Untangling text data mining. Paper presented at the 37th Annual Meeting of the Association for Computational Linguistics (ACL 99). University of Maryland, Baltimore.
24. Hodge, V.J., & Austin, J. (2002). Hierarchical word clustering - Automatic thesaurus generation. Neurocomputing, 48, 819-846.
25. Jackson, J.E. (1991). A users guide to principal components. New York: Wiley.
26. Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological measurement, 20, 141-151.
27. Kankar, P., Adak, S., Sarkar, A., Murali, K., & Sharma, G. (2002). MedMesh summarizer: Text mining for gene clusters. In R. Grossman, J. Han, V. Kumar, H. Mannila, & R. Motwani (Eds.), Proceedings of the Second SIAM International Conference on Data Mining. Part X: Applications. Arlington VA: SIAM
28. Karypis, G., Han, E.H., & Kumar, V. (1999). Chameleon: A hierarchical clustering algorithm using dynamic modeling [Special issue]. IEEE Computer, 32(8), 68-75.
29. Karypis, G. (2004). CLUTO - A clustering toolkit. Retrieved February 18, 2004, from http://www.cs.umn.edu/~cluto
30. Kendall, M.G., & Lawley, D.N. (1956). The principles of factor-analysis. Journal of the Royal Statistical Society Series A-General, 119(1), 83-84.
31. Khare, A. (2003). Connecting word clusters to represent concepts with application to web searching. Knowledge-based intelligent information and engineering systems, Part 1. Lecture Notes in Artificial Intelligence, 2773, 816-823.
32. Kim, W., & Wilbur, W.J. (2001). Corpus-based statistical screening for content-bearing terms. Journal of the American Society for Information Science and Technology, 52(3), 247-259.
33. Ko, Y., Kim, K., & Seo, J. (2003). Topic keyword identification for text summarization using lexical clustering. IEICE Transactions on Information and Systems, E86D(9), 1695-1701.
34. Kogan, J., Nicholas, C., & Volkovich, V. (2003). Text mining with information - Theoretic clustering. Computing in Science & Engineering, 5(6), 52-59.
35. Kostoff, R.N., Eberhart, H.J., & Toothman, D.R. (1997). Database tomography for information retrieval. Journal of Information Science, 23(4), 301-311.
36. Kostoff, R.N., Green, K.A., Toothman, D.R., & Humenik, J.A. (2000a). Database tomography applied to an aircraft science and technology investment strategy. Journal of Aircraft, 37(4), 727-730.
37. Kostoff, R.N., Braun, T., Schubert, A., Toothman, D.R., & Humenik, J.A. (2000b). Fullerene roadmaps using bibliometrics and database tomography. Journal of Chemical Information and Computer Science, 40(1), 19-39.
38. Kostoff, R.N., & DeMarco, R.A. (2001a). Science and technology text mining. Analytical Chemistry, 73(13), 370-378A.
39. Kostoff, R.N., Del Rio, J.A., García, E.O., Ramírez, A.M., & Humenik, J.A. (2001b). Citation mining: Integrating text mining and bibliometrics for research user profiling. Journal of the American Society for Information Science and Technology, 52(13), 1148-1156.
40. Kostoff, R.N., Toothman, D.R., Eberhart, H.J., & Humenik, J.A. (2001c). Text mining using database tomography and bibliometrics: A review. Technology Forecasting and Social Change, 68(3), 223-253.
41. Kostoff, R.N., Tshiteya, R., Pfeil, K.M., & Humenik, J.A. (2002). Electro-chemical power source roadmaps using bibliometrics and database tomography. Journal of Power Sources, 110(1), 163-176.
42. Kostoff, R.N. (2003a). Text mining for global technology watch. In M. Drake (Ed.), Encyclopedia of library and information science (2nd ed., Vol. 4, pp. 2789-2799). New York: Marcel Dekker.
43. Kostoff, R.N. (2003b). Stimulating innovation. In L.V. Shavinina (Ed.), International handbook of innovation. Oxford, UK: Elsevier.
44. Kostoff, R.N. (2003c). Bilateral asymmetry prediction. Medical Hypotheses, 61(2), 265-266.
45. Kostoff, R.N. (2003d). The practice and malpractice of stemming. Journal of the American Society for Information Science and Technology, 54(10), 984-985.
46. Kostoff, R.N., Shlesinger, M.F., & Malpohl, G. (2004a). Fractals roadmaps using bibliometrics and database tomography. Fractals, 12(1), 1-16.
47. Kostoff, R.N., Shlesinger, M., & Tshiteya, R. (2004b). Nonlinear dynamics roadmaps using bibliometrics and Database Tomography. International Journal of Bifurcation and Chaos, 14(1), 61-92.
48. Kostoff, R.N., Bedford, C.W., Del Rio, J. A., Cortes, H., & Karypis, G. (2004c). Macromolecule mass spectrometry: Citation mining of user documents. Journal of the American Society for Mass Spectrometry, 15(3), 281-287.
49. Kostoff, R.N., & Block, J.A. (2004d). Context-dependent conflation, text filtering, and clustering (Defense Technical Information Center, Technical Report number ADA426072). Fort Belvoir, VA: Defense Technical Information Center.
50. Kostoff, R.N., & Shlesinger, M.F. (2005). CAB - Citation-assisted background. Scientometrics, 62(2), 199-212.
51. Kuhnhold, M. (2000). The concept of "text mining." Wirtschaftsinformatik, 42(2), 175-179.
52. Lagus, K., Honkela, T., Kaski, S., & Kohonen, T. (1999). Websom for textual data mining. Artificial Intelligence Review, 13(5-6), 345-364.
53. Losiewicz, P., Oard, D., & Kostoff, R.N. (2000). Textual data mining to support science and technology management. Journal of Intelligent Information Systems, 15, 99-119.
54. McArdle, J.J. (1990). Principles versus principals of structural factor-analyses. Multivariate Behavioral Research, 25(1), 81-87.
55. Narin, F. (1976). Evaluative bibliometrics: The use of publication and citation analysis in the evaluation of scientific activity (Publication No. NSF C-637, NTIS Accession No. PB252339/AS). Washington, DC: National Science Foundation.
56. Narin, F., Olivastro, D., & Stevens, K.A. (1994). Bibliometrics theory, practice and problems. Evaluation Review, 18(1), 65-76.
57. Perrin, P., & Petry, F.E. (2003). Extraction and representation of contextual information for knowledge discovery in texts. Information Sciences, 151, 125-152.
58. Porter, M.F. (1980). An algorithm for suffix stripping. Program, 14(3), 130-137.
59. Rasmussen, E. (1992). Clustering algorithms. In W.B. Frakes & R. Baeza-Yates (Eds.), Information retrieval data structures and algorithms. Englewood Cliffs, NJ: Prentice Hall.
60. Salton, G., & Buckley, C. (1998). Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5), 513-523.
61. Schenker, A., Last, M., Bunke, H., & Kandel, A. (2003). Graph representations for web document clustering. Pattern Recognition and Image Analysis, Proceedings Lecture Notes in Computer Science, 2652, 935-942.
62. Schroeder, M. (1991). Fractals, chaos, power laws: Minutes from an infinite paradise. New York: W.H. Freeman.
63. Schubert, A., Glanzel, W., & Braun, T. (1987). Subject field characteristic citation scores and scales for assessing research performance. Scientometrics, 12(5-6), 267-291.
64. Stegmann, J., & Grohmann, G. (2003). Hypothesis generation guided by co-word clustering. Scientometrics, 56(1), 111-135.
65. Steinbach, M., Karypis, G., & Kumar, V.A. (2000). Comparison of document clustering techniques (Technical Report #00-034). Department of Computer Science and Engineering, University of Minnesota, Minneapolis.
66. Stensmo, M. (2002). A scalable and efficient probabilistic information retrieval and text mining system. Artificial Neural Networks - ICANN 2000 Lecture Notes in Computer Science, 2415, 643-648.
67. Swanson D.R. (1986). Fish oil, Raynaud's Syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, 30(1), 7-18.
68. Swanson, D.R., & Smalheiser, N.R. (1997). An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artificial Intelligence, 91(2), 183-203.
69. Text Retrieval Conference (TREC). (2003). Retrieved December 8, 2003, from http://trec.nist.gov/
70. Trybula, W.J. (1999). Text mining. Annual Review of Information Science and Technology, 34, 385-419.
71. Viator, J.A., & Pestorius, F.M. (2001). Investigating trends in acoustics research from 1970-1999. Journal of the Acoustical Society of America, 109(5), 1779-1783.
72. Visa, A. (2001). Technology of text mining. Machine Learning and Data Mining in Pattern Recognition, Lecture Notes in Artificial Intelligence, 2123, 1-11.
73. Wang, B.B., McKay, R.I., Abbass, H.A., & Barlow, M. (2003). A comparative study for domain ontology guided feature extraction. In M. Oudshoorn (Ed.), Twenty-Fifth Australian Computer Science Conference (ACSC2003). Adelaide, Australia: Australian Computer Society.
74. Weeber, M., Klein, H., Aronson, A.R, Mork, J.G, de Jong-van den Berg, L.T.W, & Vos, R. (2000). Text-based discovery in biomedicine: The architecture of the DAD-system. Journal of the American Medical Informatics Association (Suppl. S), 903-907
75. Weiss, S.M., Apte, C., Damerau, F.J., Johnson, D.E., Oles, F.J., Goetz, T., et al. (1999). Maximizing text-mining performance. IEEE Intelligent Systems & Their Applications, 14(4), 63-69.
76. Wilbur, W.J., & Sirotkin, K. (1992). The automatic identification of stop words. Journal of Information Science, 18(1), 45-55.
77. Wilbur, W.J., & Yang, Y.M. (1996). An analysis of statistical term strength and its use in the indexing and retrieval of molecular biology texts. Computers in Biology and Medicine, 26(3), 209-222.
78. Willet, P. (1988). Recent trends in hierarchical document clustering: A critical review. Information Processing and Management, 24, 577-597.
79. Yalcin, I., & Amemiya, Y. (2001). Nonlinear factor analysis as a statistical method. Statistical Science, 16(3), 275-294.
80. Zamir, O., & Etzioni, O. (1998). Web document clustering: A feasibility demonstration. In D. Harman & P. Over (Eds.), Proceedings of the 21st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'98) (pp. 46-54). New York: ACM.
81. Zhu, D.H., & Porter, A.L. (2002). Automated extraction and visualization of information for technological intelligence and forecasting. Technological Forecasting and Social Change, 69(5), 495-506.