LSISOM - A latent semantic indexing approach to self-organizing maps of document collections

Neural Processing Letters

Volumn 19, Issue 2, 2004, Pages 157-173

  Ampazis, Nikolaus ^a   Perantonis, Stavros J ^b  

^a UNIVERSITY OF THE AEGEAN   (Greece)

^b INSTITUTE OF INFORMATICS AND TELECOMMUNICATIONS   (Greece)

Author keywords

Data representation; Document clustering; Information retrieval; Latent semantic indexing; Self organizing maps; Unsupervised learning

Indexed keywords

DATA PROCESSING; ENCODING (SYMBOLS); INDEXING (OF INFORMATION); INFORMATION RETRIEVAL; LEARNING SYSTEMS; MATHEMATICAL MODELS; SEMANTICS; VECTORS;

DATA REPRESENTATION; DOCUMENT CLUSTERING; LATENT SEMANTIC INDEXING; UNSUPERVIZED LEARNING;

SELF ORGANIZING MAPS;

EID: 3543087974     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:NEPL.0000023449.95030.8f     Document Type: Article

References (27)

1. Ampazis, N. and Perantonis, S. J.: Evaluation of dimensionality reduction techniques for SOM clustering of textual data, In: Artificial Intelligence and Applications (AIA 2002), Malaga, Spain, 2001.
2. Berry, M. W.: Large scale singular value computations, International Journal of Supercomputer Applications 6(1) (1992).
3. Berry, M. W., Dumais, S. T. and O'Brien, G. W.: Using linear algebra for intelligent information retrieval, SIAM Review 37(4) (1995), 573-595.
4. Bingham, E. and Mannila, H.: Random projection in dimensionality reduction: applications to image and text data, In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), pp. 245-250, San Francisco, CA, USA, 2001.
5. Chen, H., Schuffels, C. and Orwig, R.: Internet categorization and search: A machine learning approach, Journal of Visual Communication and Image Representation 7(1) (1996), 88-102, Special Issue on Digital Libraries.
6. Davies, D. L. and Bouldin, D.: A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence 1 (1979), 224-227.
7. Deerwester, S., Dumais, S., Furnas, G., Landauer, T. and Harshman, R.: Indexing by latent semantic analysis, Journal of the American Society for Information Science 41(6) (1990), 391-407.
8. Ding, C.: A similarity based probability model for latent semantic indexing, In: ACM SIGIR Conference Proceedings (1999).
9. Honkela, T., Kaski, S., Lagus, K. and Kohonen, T.: Newsgrpup exploration with WEBSOM method and browsing interface, Technical Report No. A32, Espoo, Finland. 1996.
10. Honkela, T., Kaski, S., Lagus, K. and Kohonen, T.: WEBSOM-self-organizing maps of document collections, In: Proceedings of WSOM'97, Workshop on Self-organizing Maps, Espoo, Finland, June 4-6. pp. 310-315, Espoo, Finland, Helsinki University of Technology, Neural Networks Research Centre, 1997.
11. Jain, A. K. and Dubes, R. C.: Algorithms for Clustering Data. Prentice Hall, New Jersey, 1988.
12. Jing, Y. and Croft, W. B.: An association thesaurus for information retrieval, In: Proceedings of RIAO-94, Fourth International Conference Recherche d'Information Assistee par Ordinateur, pp. 146-160, New York, US, 1994.
13. Johnson, W. B. and Lindenstrauss, J.: Extensions of Lipshitz mapping into Hubert space, Contemp. Math. 26 (1984), 189-206.
14. Kaski, S., Honkela, T., Lagus, K. and Kohonen, T.: Creating an order in digital libraries with self-organizing maps, In: Proceedings of WCNN'96, World Congress on Neural Networks, September 15-18, pp. 814-817, San Diego, California. Mahwah, NJ, Lawrence Erlbaum and INNS Press, 1996.
15. Kohonen, T.: Self-organization and Associative Memory, Springer-Verlag, N.Y, 3rd edition. 1989.
16. Kohonen, T., Kaski, S., Lagus, K., Salojrvi, J., Honkela, J., Paatero, V. and Saarela, A.: Self organization of a massive document collection, IEEE Transactions on Neural Networks 11(3) (2000), 574-585. Special Issue on Neural Networks for Data Mining and Knowledge Discovery.
17. Lagus, K., Honkela, T., Kaski, S. and Kohonen, T.: Self-organizing maps of document collections: A new approach to interactive exploration, In: Simoudis, E., Han, J. and U. Fayyad (eds.), Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, California, pp.238-243, 1996a.
18. Lagus, K., Kaski, S., Honkela, T. and Kohonen, T.: Browsing digital libraries with the aid of self-organizing maps, In: Proceedings of the Fifth International World Wide Web Conference WWW5, May 6-10, pp. 71-79, Paris, France, Vol. Poster Proceedings. EPGL, 1996b.
19. MacCuish, J. D., N. C. and MacCuish, N. E.: A pattern recognition approach to understanding the multilayer perceptron, J. Chemical Information and Computer Sciences 41 (2001), 134-146.
20. Merkl, D. and Tjoa, A. M.: The representation of semantic similarity between documents by using maps: application of an artificial neural network to organize software libraries, In: FID'94, General Assembly Conference and Congress of the International Federation for Information and Documentation, 1994.
21. Papadimitriou, C. H., Raghavan, P., Tamaki, H. and Vempala, S.: Latent semantic indexing: a probabilistic analysis, JCSS 61(2) (2000), 217-235.
22. Rauber, A. and Merkl, D.: Using self-organizing maps to organize document archives and to characterize subject matter: how to make a map tell the news of the world, In: Database and Expert Systems Applications (DEXA 1999), pp. 302-311, 1999.
23. Ritter, H. and Kohonen, T.: Self-organizing semantic maps, Biol. Cyb. 61 (4) (1989), 241-254.
24. Salton, G. and McGill, M.J.: Introduction to Modem Information Retrieval, New York, 1983.
25. Scholtes, J. C.: Unsupervised learning and the information retrieval problem, In: IJCNN'91, International Joint Conference on Neural Networks, pp. 95-100, Singapore, 1991.
26. Vesanto, J., Himberg, J., Alhoniemi, E. and Parhankangas, J.: SOM Toolbox for Matlab 5. Report No. A57, Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland, 2000.
27. Lin, X., D. S. and Marchionini, G.: A self-organizing semantic map for information retrieval, In: Fourteenth Annual International ACM/SIGIR Conference on R & D In Information Retrieval, pp. 262-269, 1991.