Tree view self-organisation of web content - Institute for Water Education

Neurocomputing

Volumn 63, Issue SPEC. ISS., 2005, Pages 415-446

  Freeman, Richard T ^a   Yin, Hujun ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Browsing and navigation; Document clustering; Information retrieval; Knowledge management; Self organising maps

Indexed keywords

DATA STRUCTURES; HIERARCHICAL SYSTEMS; INFORMATION ANALYSIS; PROGRAM DOCUMENTATION; SELF ORGANIZING MAPS; TEXT PROCESSING;

DOCUMENT CLUSTERING; HIERARCHICAL CLUSTERING; TEXT DOCUMENTS; TOPOLOGICAL TAXONOMY TREE; WEB CONTENT;

WEB BROWSERS;

ARTICLE; AUTOMATION; COMPUTER ANALYSIS; COMPUTER PROGRAM; DATA ANALYSIS; DOCUMENTATION; INTERMETHOD COMPARISON; INTERNET; ONLINE SYSTEM; ORGANIZATION; PRIORITY JOURNAL; TAXONOMY; VALIDATION PROCESS;

EID: 12144252036     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2004.07.005     Document Type: Article

References (65)

1. D. Alahakoon S.K. Halgamuge B. Srinivasan Dynamic self-organizing maps with controlled growth for knowledge discovery IEEE Trans. Neural Networks 11 2000 601-614
2. N. Allinson H. Yin L. Allinson J. Slack Advances in Self-Organising Maps 2001 Springer London, UK
3. J. Blackmore, R. Miikkulainen, Incremental grid growing: Encoding high-dimensional structure into a two-dimensional feature map, ICNN '93, IEEE Press, New York, Vol. 1, 1993, pp. 450-455.
4. D. Boley Principal direction divisive partitioning Data Min. Knowl. Disc. 2 1998 325-344
5. H. Chen A.L. Houston R.R. Sewell B.R. Schatz Internet browsing and searching: User evaluations of category map and concept space techniques J. Am. Soc. Inf. Sci. 49 1998 582-603
6. H. Chen C. Schuffels R. Orwig Internet categorization and search: A self-organizing approach J. Vis. Commun. Image Represent. 7 1996 88-102
7. C.J. Crouch, A cluster-based approach to thesaurus construction, SIGIR '88, ACM, New York, 1988, pp. 309-320.
8. D.R. Cutting, D.R. Karger, J.O. Pederson, J.W. Tukey, Scatter/gather: A cluster-based approach to browsing large document collections, SIGIR '92, ACM, New York, 1992, pp. 318-329.
9. M. Dash, H. Liu, J. Yao, Dimensionality reduction of unsupervised data, ICTAI '97, IEEE Computer Society Press, Silver Spring, MD, 1997, pp. 532-539.
10. S. Deerwester S.T. Dumais G.W. Furnas T.K. Landauer R. Harshman Indexing by latent semantic analysis J. Am. Soc. Inf. Sci. 41 1990 391-407
11. W. Deng, W. Wu, Document categorization and retrieval using semantic microfeatures and growing cell structures, DEXA '01, IEEE Computer Society Press, Silver Spring, MD, 2001, pp. 270-274.
12. M. Dittenbach A. Rauber D. Merkl Recent advances with the growing hierarchical self-organising map in: N. Allinson H. Yin L. Allinson J. Slack (Eds.) Advances in Self-Organising Maps 2001 Springer Berlin 140-145
13. M. Franz, J.S. McCarley, T. Ward, Z. Wei-Jing, Unsupervised and supervised clustering for topic tracking, SIGIR '01, ACM, New York, 2001, pp. 310-317.
14. R. Freeman, H. Yin, Self-organising maps for hierarchical tree view document clustering using contextual information, IDEAL 2002, Lecturer Notes in Computer Science, Vol. 2412, Springer, Berlin, 2002, pp. 123-128.
15. R. Freeman, H. Yin, N.M. Allinson, Self-organising maps for tree view based hierarchical document clustering, IJCNN '02, IEEE Press, New York, Vol. 2, 2002, pp. 1906-1911.
16. B. Fritzke, Unsupervised clustering with growing cell structures, IJCNN '91, IEEE Press, New York, Vol. 2, 1991, pp. 531-536.
17. B. Fritzke Kohonen feature maps and growing cell structures-a performance comparison Adv. Neural Inf. Process. Systems 5 1993 123-130
18. B. Fritzke Growing grid - a self-organizing network with constant neighbourhood range and adaptation strength Neural Process. Lett. 2 1995 9-13
19. B. Fritzke A growing neural gas network learns topologies Adv. Neural Inf. Process. Systems 7 1995 625-632
20. K. Fuka, R. Hanka, Feature set reduction for document classification problems, IJCAI-01, 2001.
21. D. Goren-Bar, T. Kuflik, D. Lev, P. Shoval, Automating personal categorization using artificial neural networks, UM 2001, Vol. 2109, Springer, Berlin, 2001, pp. 188-198.
22. S. Haykin Neural Networks a Comprehensive Foundation 1999 Prentice-Hall Englewood Cliff, NJ
23. V.J. Hodge J. Austin Hierarchical growing cell structures TreeGCS IEEE Trans. Knowl. Data Eng. 13 2001 207-218
24. V.J. Hodge J. Austin Hierarchical word clustering - automatic thesaurus generation Neurocomputing 48 2002 819-846
25. T. Honkela, WEBSOM self-organizing maps of document collections, WSOM '97, Helsinki University of Technology, 1997, pp. 310-315.
26. T. Honkela, S. Kaski, K. Lagus, T. Kohonen, Exploration of full-text databases with self-organizing maps, ICNN '96, IEEE Press, New York, Vol. 1, 1996, pp. 56-61.
27. K.S. Jones Information Retrieval Experiment 1981 Butterworth London
28. S. Kaski, Dimensionality reduction by random mapping: Fast similarity computation for clustering, IJCNN '98, Vol. 1, IEEE Press, New York, 1998, pp. 413-418.
29. S. Kaski T. Honkela K. Lagus T. Kohonen WEBSOM-self-organizing maps of document collections Neurocomputing 21 1998 101-117
30. J.L. Klavans, C. Jacquemin, E. Tzoukermann, A natural language approach to multi-word term conflation, DELOS, ERCIM, 1997.
31. T. Kohonen, Exploration of very large databases by self-organizing maps, ICNN '97, Vol. 1, IEEE Press, New York, 1997, pp. 1-6.
32. T. Kohonen Self Organizing Maps 1997 Springer Heidelberg
33. T. Kohonen S. Kaski K. Lagus J. Salojarvi J. Honkela V. Paatero A. Saarela Self organization of a massive document collection IEEE Trans. Neural Networks 11 2000 574-585
34. R. Kondadadi, R. Kozma, A modified fuzzy ART for soft document clustering, IJCNN '02, Vol. 3, IEEE Press, New York, 2002, pp. 2545-2549.
35. K. Lagus, S. Kaski, Keyword selection method for characterizing text document maps, ICANN '99, Vol. 1, IEE, 1999, pp. 371-376.
36. X. Lin, D. Soergel, G. Marchionini, A self-organizing semantic map for information retrieval, SIGIR, ACM, New York, 1991, pp. 262-269.
37. Y.S. Maarek D.M. Berry G.E. Kaiser An information retrieval approach for automatically constructing software libraries IEEE Trans. Software Eng. 17 1991 800-813
38. K.J. MacLeod W. Robertson A neural algorithm for document clustering Inf. Process. Manage. 27 1991 337-346
39. T. Mandl Tolerant information retrieval with backpropagation networks Neural Comput. Appl. 9 2000 280-289
40. D. Merkl, Content-based software classification by self-organization, ICNN, Vol. 2, IEEE Press, New York, 1995, pp. 1086-1091.
41. D. Merkl, Exploration of text collections with hierarchical feature maps, SIGIR, ACM, New York, 1997, pp. 186-195.
42. D. Merkl Text classification with self-organizing maps Neurocomputing 21 1998 61-77
43. D. Merkl A. Rauber Document classification with unsupervised artificial neural networks in: F. Crestani G. Pasi (Eds.) Soft Computing in Information Retrieval 2000 Physica-Verlag Wurzburg 102-121
44. R. Miikkulainen Script recognition with hierarchical feature maps Connect. Sci. J. Neural Comput. Artif. Intell. Cognitive Res. 2 1990 83-101
45. E. Miller, D. Shen, J. Liu, C. Nicholas, Performance and scalability of a large-scale N-gram based information retrieval system, J. Digital Inf. 1 (2000).
46. P. Mitra C.A. Murthy S.K. Pal unsupervised feature selection using feature similarity IEEE Trans. Pattern Anal. Mach. Intell. 24 2002 301-312
47. A. Nurnberger, M. Detyniecki, Visualizing changes in data collections using growing self-organizing maps, IJCNN '02, Vol. 2, 2002, pp. 1912-1917.
48. M.F. Porter An algorithm for suffix stripping Program-Autom. Libr. Inf. Systems 14 1980 130-137
49. E.M. Rasmussen, P. Willett, Non-hierarchic document clustering using the ICL distributed array processor, SIGIR, ACM, New York, 1987, pp. 132-139.
50. A. Rauber, LabelSOM: On the labeling of self-organizing maps, IJCNN '99, International Joint Conference on Neural Networks, Proceedings, Vol. 2, IEEE Press, New York, 1999, pp. 3527-3532.
51. A. Rauber, H. Bina, "'Andreas, Rauber'? Conference pages are over there, German documents on the lower left...": An "old-fashioned" approach to Web search results visualization, DEXA 2000, IEEE Computer Society Press, Silver Spring, MD, 2000, pp. 615-619.
52. A. Rauber D. Merkl M. Dittenbach The growing hierarchical self-organizing map exploratory analysis of high-dimensional data IEEE Trans. Neural Networks 13 2002 1331-1341
53. D.G. Roussinov H. Chen A scalable self-organizing map algorithm for textual classification a neural network approach to thesaurus generation, Cc-Ai J. Integrated Study Artif. Intell. Cognitive Sci. Appl. Epistemol. 15 1998 81-111
54. D.G. Roussinov H. Chen Information navigation on the Web by clustering and summarizing query results Inf. Process. Manage. 37 2001 789-816
55. M. Sahami, S. Yusufali, M.Q.W. Baldonado, SONIA: A service for organizing networked information autonomously, Digital Libraries, ACM, New York, 1998, pp. 200-209.
56. G. Salton Automatic Text Processing-the Transformation, Analysis, and Retrieval of Information by Computer 1989 Addison-Wesley Reading, MA
57. P. Schauble, Thesaurus based concept spaces, SIGIR, ACM, New York, 1987, pp. 254-262.
58. M. Steinbach, G. Karypis, V. Kumar, A Comparison of document clustering techniques, Proceedings of the World Text Mining Conference, KDD 2000, Boston, 2000.
59. H. Tanaka T. Kumano N. Uratani T. Ehara An efficient document clustering algorithm and its application to a document browser Inf. Process. Manage. 35 1999 541-557
60. C.J. Van Rijsbergen Information Retrieval 1979 Butterworth London
61. C.J. Van Rijsbergen W.B. Croft Document clustering an evaluation of some experiments with the Cranfield 1400 collection Inf. Process. Manage. 11 1975 171-182
62. P. Willett Recent trends in hierarchic document clustering a critical review Inf. Process. Manage. 24 1988 577-597
63. H. Ye B.W.N. Lo A visualised software library nested self-organising maps for retrieving and and browsing reusable software assets Neural Comput. Appl. 9 2000 266-279
64. H. Ye B.W.N. Lo Towards a self-structuring software library IEE Proc. Software 148 2001 45-55
65. O. Zamir, O. Etzioni, Web document clustering: A feasibility demonstration, SIGIR, ACM, New York, 1998, pp. 46-54.