REFERENCES

data mining: opportunities and challenges
Chapter VIII - Mining Text Documents for Thematic Hierarchies Using Self-Organizing Maps
Data Mining: Opportunities and Challenges
by John Wang (ed) 
Idea Group Publishing 2003
Brought to you by Team-Fly

Apte, C., Damerau, F., & Weiss, S. M. (1994). Automated learning of decision rules for text categorization. ACM Trans. Information Systems, 12(3), 233 251.

Chen, A., He, J. Z., Xu, L. J., Gey, F. C., & Meggs, J. (1997). Chinese text retrieval without using a dictionary. 20th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42 49, ACM.

Clifton, C., & Cooley, R. (1999). TopCat: Data mining for topic identification in a text corpus. European Conference on Principles of Data Mining and Knowledge Discovery, PKDD'99. pp. 174 183. Springer, Lecture Notes in Computer Science, Vol. 1704.

Cohen, W. W., & Singer, Y. (1996). Context-sensitive learning methods for text categorization. 19th International ACM SIGIR Conference on Research and Develop-ment in Information Retrieval, pp. 307 315, ACM.

Cox, T. F., & Cox, M. A. A. (1994). Multidimensional scaling. London: Chapman & Hall.

Dai, Y., Loh, T. E., & Khoo, C. (1999). A new statistical formula for Chinese text segmentation incorporating contextual information. 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 82 89, ACM.

Deerwester, S., Dumais, S., Furnas, G., & Landauer, K. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 40(6), 391 407.

Feldman, R., Dagan, I., & Hirsh, H. (1998). Mining text using keyword distributions. Journal of Intelligent Information Systems, 10, 281 300.

Grobelnik, M., & Mladenic, D. (1998). Efficient text categorization. Text Mining Workshop on ECML-98. Chemnitz, Germany:Springer, Lecture Notes in Computer Science, Vol. 1398.

Hearst, M. A., & Karadi, C. (1997). Cat-a-Cone: An interactive interface for specifying searches and viewing retrieval results using a large category hierarchy. 20th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 246 255, ACM.

Hearst, M. A., & Plaunt, C. (1993). Subtopic structuring for full-length document access. 16th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 59 68, ACM.

Hofmann, T. (1999). The cluster-abstraction model: Unsupervised learning of topic hierarchies from text data. International Joint Conference on Artificial Intelligence, IJCAI'99, pp. 682 687, Morgan Kaufmann.

Huang, X., & Robertson, S. E. (1997a). Experiments on large test collections with probabilistic approaches to Chinese text retrieval. 2nd International Workshop on Information Retrieval With Asian Languages. Tsukuba, Japan, pp. 129 140, Taiwan: Academia Sinica.

Huang, X., & Robertson, S. E. (1997b). Okapi Chinese text retrieval experiments at TREC-6. 6th Text REtrieval Conference, TREC-6. pp. 137 142, National Institute of Standards and Technology (NIST), special publication 500-240.

Jolliffe, I. T. (1986). Principal component analysis. Berlin: Springer-Verlag.

Kaski, S., Honkela, T., Lagus, K., & Kohonen, T. (1998). WEBSOM-Self-organizing maps of document collections. Neurocomputing, 21, 101 117.

Kohonen, T. (1997). Self-Organizing Maps. Berlin: Springer-Verlag.

Lam, W., Ruiz, M., & Srinivasan, P. (1999). Automatic text categorization and its application to text retrieval. IEEE Trans. Knowledge and Data Engineering, 11(8), 865 879.

Larkey, L. S., & Croft, W. B. (1996). Combining classifiers in text categorization. 19th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 289 297, ACM.

Lee, C. H., & Yang, H. C. (1999). A Web text-mining approach based on self-organizing map. ACM CIKM'99 2nd Workshop on Web Information and Data Management. Kansas City, MI:, pp. 59 62, Taiwan: Academia Sinica.

Lewis, D. D. (1992). Feature selection and feature extraction for text categorization. Speech and Natural Language Workshop. Arden House, 212 217.

Lewis, D. D., Schapire, R. E., Callan, J. P., & Papka, R. (1996). Training algorithms for linear text classifiers. 19th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 298 306, Morgan Kaufmann.

Lin, C. Y. (1995). Knowledge-based automatic topic identification. Meeting of the Association for Computational Linguistics, ACL 95, pp. 308 310, Morgan Kaufmann.

McCallum, A., & Nigam, K. (1999). Text classification by bootstrapping with keywords, EM and shrinkage. ACL '99 Workshop for Unsupervised Learning in Natural Language Processing, pp. 52 58, Morgan Kaufmann.

Mehnert, R. (1997). Federal agency and federal library reports. Providence, NJ: National Library of Medicine.

Nie, J. Y., Brisebois, M., & Ren, X. (1996). On Chinese text retrieval. 19th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 225 233, ACM.

Ponte, J. M., & Croft, W. B. (1997). Text segmentation by topic. European Conference on Digital Libraries, ECDL'97. Lecture Notes in Computer Science, Vol. 1324, Springer, pp.113 125.

Rajaraman, K., Lai, K. F., & Changwen, Y. (1997). Experiments on proximity based Chinese text retrieval in TREC 6. 6th Text REtrieval Conference, TREC-6, National Institute of Standards and Technology (NIST), special publication 500-240, pp. 559 576.

Rauber, A., & Merkl, D. (1999). Using self-organizing maps to organize document archives and to characterize subject matter: How to make a map tell the news of the world. 10th International Conference on Database and Expert Systems Applica-tions, Lecture Notes in Computer Science, Vol. 1677, Springer, pp. 302 311.

Rizzo, R., Allegra, M., & Fulantelli, G. (1999). Hypertext-like structures through a SOM network. HYPERTEXT '99, Proceedings of the 10th ACM Conference on Hypertext and Hypermedia: Returning to Our Diverse Roots, February 21-25, 1999, Darmstadt, Germany:, ACM, pp. 71 72.

Rocchio, J. J. (1966). Document retrieval systems optimization and evaluation, Ph.D. Thesis, Harvard University, Cambridge. MA:.

Salton, G. (1971). Cluster search strategies and the optimization of retrieval effectiveness. In G. Salton (eds.), The SMART retrieval system - experiment in automatic document processing. Englewood Cliffs, NJ: Prentice-Hall.

Salton, G. & Lesk, M. (1971). Information analysis and dictionary construction. In G. Salton (ed.), The SMART retrieval system-experiments in automatic document processing. Englewood Cliffs, NJ: Prentice-Hall.

Salton, G. & McGill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.

Salton, G. & Singhal, A. (1994). Automatic text theme generation and the analysis of text structure. Technical Report TR 94-1438, Dept. Computer Science, Cornell University, Ithaca, NY.

Weigend, A. S., Wiener, E. D., & Pedersen, J. O. (1999). Exploiting hierarchy in text categorization. Information Retrieval, 1(3), 193 216.

Wu, Z. M., & Tseng, G. (1993). Chinese text segmentation for text retrieval: achievements and problems. Journal of the American Society for Information Science, 44(9), 532 542.

Wu, Z. M., & Tseng, G. (1995). An automatic Chinese text segmentation system for full text retrieval. Journal of the American Society for Information Science, 46(2), 83 96.

Brought to you by Team-Fly


Data Mining(c) Opportunities and Challenges
Data Mining: Opportunities and Challenges
ISBN: 1591400511
EAN: 2147483647
Year: 2003
Pages: 194
Authors: John Wang

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net