Semantic smoothing of document models for agglomerative clustering

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 2922-2927

  Zhou, Xiaohua ^a   Zhang, Xiaodan ^a   Hu, Xiaohua ^a  

^a DREXEL UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGGLOMERATIVE CLUSTERING; CLUSTERING QUALITY; CONTEXT-SENSITIVE; COSINE SIMILARITY MEASURES; KULLBACK LEIBLER DIVERGENCE; NEAREST NEIGHBORS; SIMILARITY MEASURE; SMOOTHING METHODS;

ARTIFICIAL INTELLIGENCE; DATA PROCESSING; SEMANTICS;

CLUSTER ANALYSIS;

EID: 56349148703     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bai, J., Nie, J.-Y., and Cao, G. Integrating Compound Terms in Bayesian Text Classification, Web Intelligence 2005, France.
2. Banerjee, A. and Ghosh, J. Frequency sensitive competitive learning for clustering on high-dimensional hperspheres. Proc. IEEE Int. Joint Conference on Neural Networks, pp. 1590-1595.
3. Berger, A. and Lafferty J. Information Retrieval as Statistical Translation. ACM SIGIR 1999, pp. 222-229.
4. Cao, G., Nie, J.-Y., and Bai, J. Integrating Word Relationships into Language Models, ACM SIGIR 2005, pp. 298-305.
5. Deerwester, S., Dumais, T.S., Furnas, W.G., Landauer, K.T., and Harshman, R. Indexing by Latent Semantic Analysis, Journal of the American Society of Information Science, 1990, 41(6): 391- 407
6. Dempster, A.P., Laird, N.M., and Rubin, D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 1977, 39: 1-38.
7. Kaufman, L. and Rousseuw, P.J. Finding Groups in Data: an Introduction to Cluster Analysis, John Wiley and Sons, 1990.
8. Lafferty, J. and Zhai, C. Document Language Models, Query Models, and Risk Minimization for Information Retrieval. In Proceedings of the 24th ACM SIGIR Conference on Research and Development in IR, 2001, pp.111-119.
9. Smadja, F. Retrieving collocations from text: Xtract. Computational Linguistics, 1993, 19(1), pp. 143-177.
10. Steinbach, M., Karypis, G., and Kumar, V. A Comparison of document clustering techniques . Technical Report #00-034, Department of Computer Science and Engineering, University of Minnesota, 2000.
11. Zhai, C. and Lafferty, J. Two-Stage Language Models for Information Retrieval. ACM SIGIR 2002, pp. 49-56
12. Zhai, C. and Lafferty, J. A Study of Smoothing Methods for Language Models Applied to Ad hoc Information Retrieval. ACM SIGIR 2001, pp. 334-342.
13. Zhao, Y. and Karypis, G. Criterion functions for document clustering: experiments and analysis, Technical Report, Department of Computer Science, University of Minnesota, 2001.
14. Zhong, S. and Ghosh, J. Generative model-based document clustering: a comparative study. Knowledge and Information Systems, 8(3): 374-384, 2005. (Pubitemid 41300537)
15. Zhou, X., Hu, X., Zhang, X., Lin, X., and Song, I.-Y.. Context-Sensitive Semantic Smoothing for the Language Modeling Approach to Genomic IR. ACM SIGIR 2006, Aug 6-11, 2006, pp. 170-177.
16. Zhou, X., Zhang, X., and Hu, X. The Dragon Toolkit, Data Mining and Bioinformatics Lab (DMBio), iSchool at Drexel University, PA 19104, USA http://www.ischool.drexel.edu/dmbio/dragontool