A probabilistic model for clustering text documents with multiple fields

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4425 LNCS, Issue , 2007, Pages 331-342

  Zhu, Shanfeng ^a   Takigawa, Ichigaku ^a   Zhang, Shuqin ^b   Mamitsuka, Hiroshi ^a  

^a KYOTO UNIVERSITY   (Japan)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; INFORMATION RETRIEVAL SYSTEMS; MATHEMATICAL MODELS; PROBABILITY; PROBLEM SOLVING; TEXT PROCESSING;

KEYWORDS; PROBABILISTIC MODELS; SCIENTIFIC LITERATURE; TEXT DOCUMENTS;

CLUSTER ANALYSIS;

EID: 37149049077     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71496-5_31     Document Type: Conference Paper

References (15)

1. Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison Wesley, New York. (1999)
2. Banerjee, A., Dhillon, I., Ghosh, J., Sra, S.: Generative Model-based Clustering of Directional Data, the proceedings of the SIGKDD'03, (2003) 19-28
3. Ghosh, J.: Scalable clustering methods for data mining, Handbook of data mining, editor: Ye, N., Erlbaum, L. (2003)
4. Hersh, WR., Bhupatiraju, RT., Ross, L., Johnson, P., Cohen, AM., Kraemer, DF.: TREC 2004 Genomics Track Overview, the proceedings of the Thirteenth Text REtrieval Conference (TREC 2004). editor: Voorhees, EM. and Buckland, LP. (2004)
5. Hersh, WR., Cohen, A., Bhupatiraju, RT., Johnson, P., Hearst, M.: TREC 2005 Genomics Track Overview, the proceedings of the Fourteenth Text REtrieval Conference (TREC 2005), editor: Voorhees, EM. and Buckland, LP. (2005)
6. Jain, AK., Murty, MN., Flynn, PJ.: Data Clustering: A Review. ACM Computing Surveys. 31(3) (1999) 264-323
7. Meila, M., Heckerman, D.: An Experimental Comparison of Model-Based Clustering Methods. Machine Learning. 42(1/2) (2001) 9-29
8. McCallum, A., Nigam, K.: A comparsion of event models for naive Bayes text classification. AAAI Workshop on Learning for Text Categorization. (1998) 41-48
9. Lewis, DD.: Naive (Bayes) at forty: the independence assumption in information retrieval. Proceedings of ECML-98.
10. Nelson, SJ., Schopen, M., Savage, AG., Schulman, J., Arluk, N.: The MeSH Translation Maintenance System: Structure, Interface Design, and Implementation. Proceedings of the 11th World Congress on Medical Informatics, editor: Fieschi, M. et al. (2004) 67-69
11. Rigouste, L., Cappé, O., Yvon, F.: Evaluation of a Probabilistic Method for Unsupervised Text Clustering. International Symposium on Applied Stochastic Models and Data Analysis,Brest, France. (2005)
12. Wheeler, D. et al.: Database resources of the National Center for Biotechnology Information Nucl. Acids Res. 33 (2005) D39-D45
13. Yoo, I., Hu, X.: A comprehensive comparison study of document clustering for a biomedical digital library MEDLINE. ACM/IEEE Joint Conference on Digital Libraries, JCDL 2006, editor: Marchionini, G. et al. (2006) 220-229.
14. Zhong, S., Ghosh, J.: A unified framework for model-based clustering. Journal of Machine Learning Research. 4 (2003) 1001-1037
15. Zhong, S., Ghosh, J.: Generative model-based document clustering: a comparative study. Knowledge and Information Systems. 8(3) (2005) 374-384