NaviSOM: Automatic construction of navigational structure for web pages using self-organizing maps

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2006, Pages 985-992

  Yang, Hsin Chang ^a,b   Lee, Chung Hong ^c  

^a IEEE

^b CHANG JUNG CHRISTIAN UNIVERSITY   (Taiwan)

^c NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DATA STRUCTURES; LEARNING SYSTEMS; SELF ORGANIZING MAPS;

INFORMATION FINDING; NAVISOM; THEMATIC KEYWORDS; WEB DIRECTORIES;

WORLD WIDE WEB;

EID: 40649119489     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. G. Salton and A. Singhal, "Automatic text theme generation and the analysis of text structure," Dept. Computer Science, Cornell Univ., Ithaca, NY, Tech. Rep. TR 94-1438, 1994.
2. C. Clifton and R. Cooley, "Topcat: Data mining for topic identification in a text corpus," in Proc. European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD 99), 1999, pp. 174-183.
3. J. M. Ponte and W. B. Croft, "Text segmentation by topic," in Proc. European Conference on Digital Libraries (ECDL 97), 1997, pp. 113-125.
4. C. Y. Lin, "Knowledge-based automatic topic identification," in Proc. Meeting of the Association for Computational Linguistics (ACL 95), 1995, pp. 308-310.
5. M. A. Hearst and C. Plaunt, "Subtopic structuring for full-length document access," in Proc. 16th Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, 1993, pp. 59-68.
6. A. McCallum and K. Nigam, "Text classification by bootstrapping with keywords, em and shrinkage," in Proc. ACL '99 Workshop for Unsupervised Learning in Natural Language Processing, 1999, pp. 52-58.
7. A. S. Weigend, E. D. Wiener, and J. O. Pedersen, "Exploiting hierarchy in text categorization," Information Retrieval, vol. 1, no. 3, pp. 193-216, 1999.
8. T. Hofmann, "The cluster-abstraction model: Unsupervised learning of topic hierarchies from text data," in Proc. Int'l Joint Conf. on Artificial Intelligence (IJCAI 99), 1999, pp. 682-687.
9. S. Pepper, "Navigating haystacks, discovering needles," Markup Languages: Theory and Practice, vol. 1, pp. 41-68, 1999.
10. M. Biezunski, "Introduction to topic mapping," in SGML, Europe 1997 GCA Conference, Barcelona, Spain, 1997.
11. H. H. Rath, "Technical issues on topic maps," in Proceedings of Metastructures 99 Conference, GCA, 1999.
12. G. Moore, "Topic map technology - the state of the art," in XML Europe 2000, Paris, Frace, 2000.
13. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval. New York: McGraw-Hill, 1983.
14. X. Huang and S. E. Robertson, "Experiments on large test collections with probabilistic approaches to chinese text retrieval," in Proc. the 2nd Int'l Workshop on Information Retrieval With Asian Languages, Tsukuba, Japan, 1997, pp. 129-140.
15. T. F. Cox and M. A. A. Cox, Multidimensional Scaling. London: Chapman & Hall, 1994.
16. I. T. Jolliffe, Principal Component Analysis. Berlin: Springer-Verlag, 1986.
17. S. Deerwester, S. Dumais, G. Furnas, and K. Landauer, "Indexing by latent semantic analysis," Journal of the American Society for Information Science, vol. 40, no. 6, pp. 391-407, 1990.
18. T. Kohonen, Self-Organizing Maps. Berlin: Springer-Verlag, 1997.