Multi-way distributional clustering via pairwise interactions

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 41-48

  Bekkerman, Ron ^a   El Yaniv, Ran ^b   McCallum, Andrew ^a  

^a University of Massachusetts Amherst   (United States)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMIC LANGUAGES; DATA REDUCTION; ELECTRONIC MAIL; OPTIMIZATION; SET THEORY;

DISTRIBUTIONAL CLUSTERING (MDC) ALGORITHMS; DOCUMENT CLUSTERING; MULTIPLE CLUSTERING; TOP-DOWN CLUSTERING;

LEARNING SYSTEMS;

EID: 31844439558     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102357     Document Type: Conference Paper

References (22)

1. Baker, L., & McCallum, A. (1998). Distributional clustering of words for text classification. Proceedings of SIGIR-21 (pp. 96-103).
2. Banerjee, A., Dhillon, I., Ghosh, J., Merugu, S., & Modha, D. (2004). A generalized maximum entropy approach to Bregman co-clustering and matrix approximation. Proceedings of SIGKDD-10.
3. Bekkerman, R., El-Yaniv, R., Tishby, N., & Winter, Y. (2003). Distributional word clusters vs. words for text categorization. JMLR, 5, 1183-1208.
4. Bekkerman, R., McCallum, A., & Huang, G. (2005). Automatic categorization of email into folders: benchmark experiments on Enron and SRI corpora (Technical Report IR-418). CIIR, UMass Amherst.
5. Bouvrie, J. (2004). Multi-source contingency clustering. Master's thesis, EECS, MIT.
6. Buntine, W., & Jakulin, A. (2004). Applying discrete PCA in data analysis. Proceedings of UAI-20.
7. Cheng, Y., & Church, G. (2000). Biclustering of expression data. Proceedings of ISMB-8 (pp. 93-103).
8. Dhillon, I., Mallela, S., & Kumar, R. (2003a). A divisive information theoretic feature clustering algorithm for text classification. JMLR, 3, 1265-1287.
9. Dhillon, I. S., Mallela, S., & Modha, D. S. (2003b). Information-theoretic co-clustering. Proceedings of SIGKDD-9 (pp. 89-98).
10. El-Yaniv, R., & Souroujon, O. (2001). Iterative double clustering for unsupervised and semi-supervised learning. Proceedings of NIPS-14.
11. Friedman, N., Mosenzon, O., Slonim, N., & Tishby, N. (2001). Multivariate information bottleneck. Proceedings of UAI-17.
12. Getz, G., Levine, E., & Domany, E. (2000). Coupled two-way clustering analysis of gene microarray data. PNAS, 97, 12079-84.
13. Jakulin, A., & Bratko, I. (2004). Testing the significance of attribute interactions. Proceedings of ICML-21.
14. Madeira, S., & Oliveira, A. (2004). Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Comp. Biology and Bioinformatics, 1, 24-45.
15. Mark, W., & Perrault, R. (2004). CALO: a cognitive agent that learns and organizes. https://www.calo.sri.com.
16. Peltonen, J., Sinkkonen, J., & Kaski, S. (2004). Sequential information bottleneck for finite data. Proceedings of ICML-21.
17. Pereira, F., Tishby, N., & Lee, L. (1993). Distributional clustering of English words. Proceedings of ACL-30.
18. Slonim, N., Friedman, N., & Tishby, N. (2002). Unsupervised document classification using sequential information maximization. Proceedings of SIGIR-25.
19. Slonim, N., & Tishby, N. (2000a). Agglomerative information bottleneck. Proceedings of NIPS-12 (pp. 617-623).
20. Slonim, N., & Tishby, N. (2000b). Document clustering using word clusters via the information bottleneck method. Proceedings of SIGIR-23 (pp. 208-215).
21. Tishby, N., Pereira, F., & Bialek, W. (1999). The information bottleneck method. Invited paper to the 37th Annual Allerton Conference.
22. Yeung, R. (1991). A new outlook of Shannon's information measures. IEEE transactions on information theory, 37.